research proposal in machine learning

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How to Write a Machine Learning Research Proposal

Introduction, what is a machine learning research proposal, the structure of a machine learning research proposal, tips for writing a machine learning research proposal, how to get started with writing a machine learning research proposal, the importance of a machine learning research proposal, why you should take the time to write a machine learning research proposal, how to make your machine learning research proposal stand out, the bottom line: why writing a machine learning research proposal is worth it, further resources on writing machine learning research proposals.

If you want to get into machine learning, you first need to get past the research proposal stage. We’ll show you how.

Checkout this video:

A machine learning research proposal is a document that summarizes your research project, methods, and expected outcomes. It is typically used to secure funding for your project from a sponsor or institution, and can also be used to assessment your project by peers. Your proposal should be clear, concise, and well-organized. It should also provide enough detail to allow reviewers to assess your project’s feasibility and potential impact.

In this guide, we will cover the basics of what you need to include in a machine learning research proposal. We will also provide some tips on how to create a strong proposal that is more likely to be funded.

A machine learning research proposal is a document that describes a proposed research project that uses machine learning algorithms and techniques. The proposal should include a brief overview of the problem to be tackled, the proposed solution, and the expected results. It should also briefly describe the dataset to be used, the evaluation metric, and any other relevant details.

There is no one-size-fits-all answer to this question, as the structure of a machine learning research proposal will vary depending on the specific research question you are proposing to answer, the methods you plan to use, and the overall focus of your proposal. However, there are some general principles that all good proposals should follow.

In general, a machine learning research proposal should include:

-A summary of the problem you are trying to solve and the motivation for solving it -A brief overview of previous work in this area, including any relevant background information -A description of your proposed solution and a discussion of how it compares to existing approaches -An evaluation plan outlining how you will evaluate the effectiveness of your proposed solution -A discussion of any potential risks or limitations associated with your proposed research

Useful tips for writing a machine learning research proposal:

-Your proposal should address a specific problem or question in machine learning.

-Before writing your proposal, familiarize yourself with the existing literature in the field. Your proposal should build on the existing body of knowledge and contribute to the understanding of the chosen problem or question.

-Your proposal should be clear and concise. It should be easy for non-experts to understand what you are proposing and why it is important.

-Your proposal should be well organized. Include a brief introduction, literature review, methodology, expected results, and significance of your work.

-Make sure to proofread your proposal carefully before submitting it.

A machine learning research proposal is a document that outlines the problem you want to solve with machine learning, the methods you will use to solve it, the data you will use, and the anticipated results. This guide provides an overview of what should be included in a machine learning research proposal so that you can get started on writing your own.

1. Introduction 2. Problem statement 3. Methodology 4. Data 5. Evaluation 6. References

A machine learning research proposal is a document that outlines the rationale for a proposed machine learning research project. The proposal should convince potential supervisors or funding bodies that the project is worthwhile and that the researcher is competent to undertake it.

The proposal should include:

– A clear statement of the problem to be addressed or the question to be answered – A review of relevant literature – An outline of the proposed research methodology – A discussion of the expected outcome of the research – A realistic timeline for completing the project

A machine learning research proposal is not just a formal exercise; it is an opportunity to sell your idea to potential supervisors or funding bodies. Take advantage of this opportunity by doing your best to make your proposal as clear, concise, and convincing as possible.

Your machine learning research proposal is your chance to sell your project to potential supervisors and funders. It should be clear, concise and make a strong case for why your project is worth undertaking.

A well-written proposal will convince others that you have a worthwhile project and that you have the necessary skills and experience to complete it successfully. It will also help you to clarify your own ideas and focus your research.

Writing a machine learning research proposal can seem like a daunting task, but it doesn’t have to be. If you take it one step at a time, you’ll be well on your way to writing a strong proposal that will get the support you need.

In order to make your machine learning research proposal stand out, you will need to do several things. First, make sure that your proposal is well written and free of grammatical errors. Second, make sure that your proposal is clear and concise. Third, make sure that your proposal is organized and includes all of the necessary information. Finally, be sure to proofread your proposal carefully before submitting it.

The benefits of writing a machine learning research proposal go beyond helping you get funding for your project. A good proposal will also force you to think carefully about your problem and how you plan to solve it. This process can help you identify potential flaws in your approach and make sure that your project is as strong as possible before you start.

It can also be helpful to have a machine learning research proposal on hand when you’re talking to potential collaborators or presenting your work to a wider audience. A well-written proposal can give people a clear sense of what your project is about and why it’s important, which can make it easier to get buy-in and find people who are excited to work with you.

In short, writing a machine learning research proposal is a valuable exercise that can help you hone your ideas and make sure that your project is as strong as possible before you start.

Here are some further resources on writing machine learning research proposals:

– How to Write a Machine Learning Research Paper: https://MachineLearningMastery.com/how-to-write-a-machine-learning-research-paper/

– 10 Tips for Writing a Machine Learning Research Paper: https://blog.MachineLearning.net/10-tips-for-writing-a-machine-learning-research-paper/

Please also see our other blog post on writing research proposals: https://www.MachineLearningMastery.com/how-to-write-a-research-proposal/

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Research Topics & Ideas

Artifical Intelligence (AI) and Machine Learning (ML)

If you’re just starting out exploring AI-related research topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of research topics and ideas , including examples from past studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan  to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

AI-Related Research Topics & Ideas

Below you’ll find a list of AI and machine learning-related research topics ideas. These are intentionally broad and generic , so keep in mind that you will need to refine them a little. Nevertheless, they should inspire some ideas for your project.

• Developing AI algorithms for early detection of chronic diseases using patient data.
• The use of deep learning in enhancing the accuracy of weather prediction models.
• Machine learning techniques for real-time language translation in social media platforms.
• AI-driven approaches to improve cybersecurity in financial transactions.
• The role of AI in optimizing supply chain logistics for e-commerce.
• Investigating the impact of machine learning in personalized education systems.
• The use of AI in predictive maintenance for industrial machinery.
• Developing ethical frameworks for AI decision-making in healthcare.
• The application of ML algorithms in autonomous vehicle navigation systems.
• AI in agricultural technology: Optimizing crop yield predictions.
• Machine learning techniques for enhancing image recognition in security systems.
• AI-powered chatbots: Improving customer service efficiency in retail.
• The impact of AI on enhancing energy efficiency in smart buildings.
• Deep learning in drug discovery and pharmaceutical research.
• The use of AI in detecting and combating online misinformation.
• Machine learning models for real-time traffic prediction and management.
• AI applications in facial recognition: Privacy and ethical considerations.
• The effectiveness of ML in financial market prediction and analysis.
• Developing AI tools for real-time monitoring of environmental pollution.
• Machine learning for automated content moderation on social platforms.
• The role of AI in enhancing the accuracy of medical diagnostics.
• AI in space exploration: Automated data analysis and interpretation.
• Machine learning techniques in identifying genetic markers for diseases.
• AI-driven personal finance management tools.
• The use of AI in developing adaptive learning technologies for disabled students.

AI & ML Research Topic Ideas (Continued)

• Machine learning in cybersecurity threat detection and response.
• AI applications in virtual reality and augmented reality experiences.
• Developing ethical AI systems for recruitment and hiring processes.
• Machine learning for sentiment analysis in customer feedback.
• AI in sports analytics for performance enhancement and injury prevention.
• The role of AI in improving urban planning and smart city initiatives.
• Machine learning models for predicting consumer behaviour trends.
• AI and ML in artistic creation: Music, visual arts, and literature.
• The use of AI in automated drone navigation for delivery services.
• Developing AI algorithms for effective waste management and recycling.
• Machine learning in seismology for earthquake prediction.
• AI-powered tools for enhancing online privacy and data protection.
• The application of ML in enhancing speech recognition technologies.
• Investigating the role of AI in mental health assessment and therapy.
• Machine learning for optimization of renewable energy systems.
• AI in fashion: Predicting trends and personalizing customer experiences.
• The impact of AI on legal research and case analysis.
• Developing AI systems for real-time language interpretation for the deaf and hard of hearing.
• Machine learning in genomic data analysis for personalized medicine.
• AI-driven algorithms for credit scoring in microfinance.
• The use of AI in enhancing public safety and emergency response systems.
• Machine learning for improving water quality monitoring and management.
• AI applications in wildlife conservation and habitat monitoring.
• The role of AI in streamlining manufacturing processes.
• Investigating the use of AI in enhancing the accessibility of digital content for visually impaired users.

Recent AI & ML-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic in AI, they are fairly generic and non-specific. So, it helps to look at actual studies in the AI and machine learning space to see how this all comes together in practice.

Below, we’ve included a selection of AI-related studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• An overview of artificial intelligence in diabetic retinopathy and other ocular diseases (Sheng et al., 2022)
• HOW DOES ARTIFICIAL INTELLIGENCE HELP ASTRONOMY? A REVIEW (Patel, 2022)
• Editorial: Artificial Intelligence in Bioinformatics and Drug Repurposing: Methods and Applications (Zheng et al., 2022)
• Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities, and Challenges (Mukhamediev et al., 2022)
• Will digitization, big data, and artificial intelligence – and deep learning–based algorithm govern the practice of medicine? (Goh, 2022)
• Flower Classifier Web App Using Ml & Flask Web Framework (Singh et al., 2022)
• Object-based Classification of Natural Scenes Using Machine Learning Methods (Jasim & Younis, 2023)
• Automated Training Data Construction using Measurements for High-Level Learning-Based FPGA Power Modeling (Richa et al., 2022)
• Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (Manickam et al., 2022)
• Critical Review of Air Quality Prediction using Machine Learning Techniques (Sharma et al., 2022)
• Artificial Intelligence: New Frontiers in Real–Time Inverse Scattering and Electromagnetic Imaging (Salucci et al., 2022)
• Machine learning alternative to systems biology should not solely depend on data (Yeo & Selvarajoo, 2022)
• Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests (García et al., 2022).
• Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls (Patil et al., 2022).
• Automated Machine Learning on High Dimensional Big Data for Prediction Tasks (Jayanthi & Devi, 2022)
• Breakdown of Machine Learning Algorithms (Meena & Sehrawat, 2022)
• Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device (Carolan et al., 2021)
• Machine Learning in Tourism (Rugge, 2022)
• Towards a training data model for artificial intelligence in earth observation (Yue et al., 2022)
• Classification of Music Generality using ANN, CNN and RNN-LSTM (Tripathy & Patel, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, in order for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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Trending topics for phd research proposal in machine learning.

Machine learning techniques have prompted at the forefront over the last few years due to the advent of big data. Machine learning is a precise subfield of artificial intelligence (AI) that seeks to analyze the massive data chunks and facilitate the system to learn the data automatically without the explicit support of programming. The machine learning algorithms attempt to reveal the fine-grained patterns from the unprecedented data under multiple perspectives and to build an accurate prediction model as never before. For the purpose of learning, the machine learning algorithm is sub-categorized into four broad groups include supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning. Whenever the new unseen data is fed as input to the machine learning algorithm, it automatically learns and predicts the forthcoming by exploiting the previous experience over time. Machine learning is continually liberating its potency in a broad range of applications, including the Internet of Things (IoT), computer vision, natural language processing, speech processing, online recommendation system, cyber security, neuroscience, prediction analytics, fraud detection, and so on.

• Guidelines for Preparing a Phd Research Proposal

Latest Research Proposal Ideas in Machine Learning

• Research Proposal on Attention-based Image Segmentation
• Research Proposal on Representation learning for multi-modal data
• Research Proposal on Machine Learning methods for Heart Disease Prediction
• Research Proposal on Graph Pooling and Graph Unpooling
• Research Proposal on Multi-modal and cross-domain feature fusion
• Research Proposal on Machine Learning for Multimedia classification
• Research Proposal on Neural Machine Translation with reinforcement learning
• Research Proposal on Multi-view and Multi-modal Fusion for Semi-Supervised Learning
• Research Proposal on Stock Market Prediction using Machine Learning
• Research Proposal on Adaptive radiotherapy using deep learning
• Research Proposal on Adversarial Natural Language Processing
• Research Proposal on Deep Recurrent Neural Networks
• Research Proposal on Automated image analysis and diagnosis in radiology using deep learning
• Research Proposal on Scalable and fault-tolerant data stream processing
• Research Proposal on Deep Belief Networks
• Research Proposal on Bayesian optimization for deep learning hyperparameters
• Research Proposal on Cross-domain opinion mining
• Research Proposal on Restricted Boltzmann Machines
• Research Proposal on Quantum generative models
• Research Proposal on Deep Generative Models using Belief Networks
• Research Proposal on Deep Neural Networks for Speech Recognition
• Research Proposal on Transfer learning across pattern recognition domains
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• Research Proposal on Deep Neural Networks for Computer Vision
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• Research Proposal on Multimedia representation learning
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• Research Proposal on Extreme Learning Machines
• Research Proposal on Generative adversarial networks for text generation
• Research Proposal on Transfer learning across multimedia domains
• Research Proposal on Dynamic Neural Networks
• Research Proposal on Transformer-based attention
• Research Proposal on Motion prediction and synthesis
• Research Proposal on Deep Learning for Autonomous Vehicles
• Research Proposal on Transfer Learning for Natural Language Processing
• Research Proposal on Deep reinforcement learning for image super-resolution
• Research Proposal on Long Short-Term Memory Networks
• Research Proposal on Multi-Task and Multi-Output Regression
• Research Proposal on Improved fingerprint recognition with deep learning
• Research Proposal on Radial Basis Function Networks
• Research Proposal on Domain Adaptation with Semi-Supervised Learning
• Research Proposal on Deep learning for protein structure prediction
• Research Proposal on Multi-Goal Reinforcement Learning
• Research Proposal on Reinforcement Learning with Convolutional Neural Networks
• Research Proposal on Deep learning for blood pressure prediction from wearable devices
• Research Proposal on Mutual Information Estimation
• Research Proposal on Hybrid Neural Architecture Search with combination of evolutionary and gradient-based methods
• Research Proposal on Deep Learning-based Image Restoration with Generative Adversarial Networks
• Research Proposal on Generalized Few-Shot Classification
• Research Proposal on Privacy-preserving feature engineering
• Research Proposal on Crop Yield Prediction using Deep Learning
• Research Proposal on Multimodal Deep Learning
• Research Proposal on Continuous Learning for Natural Language Processing
• Research Proposal on Structured Topic Modeling
• Research Proposal on Entity Embeddings
• Research Proposal on Attention for knowledge graph representation
• Research Proposal on Multi-agent Reinforcement Learning in Partially Observable Environments
• Research Proposal on Quantum Machine Learning
• Research Proposal on Fine-tuning of entity embeddings
• Research Proposal on Deep Reinforcement Learning for Multimodal Representation
• Research Proposal on One-Shot Learning
• Research Proposal on Object detection in 3D scenes
• Research Proposal on Multi-asset Portfolio Optimization with Deep Learning
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• Research Proposal on Video Deblurring with Deep Learning
• Research Proposal on Multiple Instance Learning
• Research Proposal on Clinical Event Prediction
• Research Proposal on Deep learning for improved identification of disease-causing genetic variations
• Research Proposal on Interpretable Machine Learning
• Research Proposal on Adversarial attacks and defenses in Convolutional Neural Networks
• Research Proposal on Named entity recognition in noisy and unstructured text
• Research Proposal on Density Estimation
• Research Proposal on Panoptic Segmentation
• Research Proposal on Improving the accuracy of treatment planning with deep learning
• Research Proposal on Imitation Learning
• Research Proposal on Deep Learning for Abnormal Event Detection in Surveillance Videos
• Research Proposal on Deep Reinforcement Learning for Microscopy Image Analysis
• Research Proposal on Active Learning
• Research Proposal on Predictive Analytics for Supply Chain Performance using Deep Learning
• Research Proposal on Face Recognition in the Wild
• Research Proposal on Object Detection using Deep Learning
• Research Proposal on Deep Learning for Compressed Sensing in Remote Sensing
• Research Proposal on Multi-task Neural Machine Translation
• Research Proposal on Image Segmentation using Deep Learning
• Research Proposal on Plant Leaf Shape and Texture Analysis with Deep Learning
• Research Proposal on Adversarial robustness in Belief Networks
• Research Proposal on Human Motion Recognition using Deep Learning
• Research Proposal on Interactive Topic Modeling with Human Feedback
• Research Proposal on Attention-based interpretation of neural networks
• Research Proposal on Dialogue Systems
• Research Proposal on Temporal Consistency Restoration in Videos using Deep Learning
• Research Proposal on Meta-Reinforcement Learning
• Research Proposal on Multimodal Representation Learning
• Research Proposal on Real-time Image Denoising with Deep Learning
• Research Proposal on Multi-Modal and Cross-Lingual Word Embeddings
• Research Proposal on Face Recognition using Deep Learning
• Research Proposal on Deep learning for blood pressure prediction in low-resource settings
• Research Proposal on Attention Mechanisms in Convolutional Neural Networks
• Research Proposal on Image Captioning using Deep Learning
• Research Proposal on Named entity recognition in a multilingual context
• Research Proposal on Graph-based pattern recognition
• Research Proposal on Named Entity Recognition
• Research Proposal on Deep learning for palmprint recognition
• Research Proposal on Action recognition in videos
• Research Proposal on Pharmacogenomics using Deep Learning
• Research Proposal on Deep learning for detecting abnormalities in medical images in radiology
• Research Proposal on Deep Learning for Cell Segmentation and Tracking
• Research Proposal on Action Recognition using Deep Learning
• Research Proposal on Transfer learning in Convolutional Neural Networks
• Research Proposal on Multi-Modal and Multi-Task Ensemble Learning
• Research Proposal on Microscopic Image Analysis using Deep Learning
• Research Proposal in Real-time analytics on big data streams
• Research Proposal on Augmentation for object detection and segmentation
• Research Proposal on Facial Expression Recognition using Deep Learning
• Research Proposal on Attention for visual data processing
• Research Proposal on Domain Adaptation for Health Record Analysis
• Research Proposal on Radiology using Deep Learning
• Research Proposal on Large-scale parallel hyperparameter optimization
• Research Proposal on Multi-modal Fusion for Facial Expression Recognition
• Research Proposal on Bioinformatics using Deep Learning
• Research Proposal on Neural Machine Translation with semantic representation
• Research Proposal on Cross-lingual Text Summarization
• Research Proposal on Text Summarization
• Research Proposal on Task-Oriented Dialogue Systems
• Research Proposal on Adversarial attacks and defenses in medical image analysis
• Research Proposal on Semantic Analysis
• Research Proposal on Image Captioning with Attention Mechanisms
• Research Proposal on Named entity recognition in low-resource languages using deep learning
• Research Proposal on Radiotherapy using Deep Learning
• Research Proposal on Deep Learning for Quantitative Image Analysis in Microscopy
• Research Proposal on Deep learning for improved classification of multi-view sequential data
• Research Proposal on Image Super Resolution Using Deep Learning
• Research Proposal on Deep Learning for Facial Emotion Recognition from Speech
• Research Proposal on Incorporating Background Knowledge in Topic Modeling
• Research Proposal on Neural Rendering
• Research Proposal on Deep learning in radiation therapy planning and optimization
• Research Proposal on Multi-modal Text Summarization
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• Research Proposal on Medical image analysis in resource-limited settings with deep learning
• Research Proposal on Meta-learning for few-shot multi-class classification
• Research Proposal on Automated evaluation of radiotherapy outcomes using deep learning
• Research Proposal on Domain-specific entity embeddings
• Research Proposal on Neural rendering for virtual and augmented reality applications
• Research Proposal on Semantic Segmentation using FCN
• Research Proposal on Improved gene expression analysis with deep learning
• Research Proposal on Multi-modal data analysis for disease prediction
• Research Proposal on Multi-level Deep Network for Image Denoising
• Research Proposal on Deep learning for image-based diagnosis in radiology
• Research Proposal on Video Inpainting with Deep Learning
• Research Proposal on Deep learning for predicting blood pressure response to treatment
• Research Proposal on Multi-agent Reinforcement Learning with Evolutionary Algorithms
• Research Proposal on Decentralized Multi-agent Reinforcement Learning
• Research Proposal on Deep Learning for Compressed Sensing Reconstruction
• Research Proposal on Deep Reinforcement Learning for Supply Chain Optimization
• Research Proposal on Multi-modal medical image analysis in radiology with deep learning
• Research Proposal on Deep Learning for Multimodal Fusion
• Research Proposal on Adversarial attacks and defenses in biometric recognition using deep learning
• Research Proposal on Deep Learning for Compressed Sensing in Wireless Communications
• Research Proposal on Human-in-the-loop Neural Architecture Search
• Research Proposal on Agricultural Resource Management with Deep Learning
• Research Proposal on Generative Models for Semi-Supervised Learning
• Research Proposal on Deep learning for predicting cancer treatment response
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• Research Proposal on Deep Reinforcement Learning for Plant Disease Detection
• Research Proposal on Fine-grained named entity recognition
• Research Proposal on Transfer learning for sentiment analysis
• Research Proposal on Deep learning for predicting protein-protein interactions
• Research Proposal on Object detection with active learning
• Research Proposal on Deep learning for improving drug discovery through in silico experimentation
• Research Proposal on Cross-lingual Image Captioning
• Research Proposal on Deep Learning for Food Safety Prediction in Agriculture
• Research Proposal on Improved epigenetic analysis using deep learning
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• Research Proposal on Plant Leaf Recognition with Computer Vision
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• Research Proposal on Deep learning for semantic-aware image super-resolution
• Research Proposal on Generative Adversarial Networks with Convolutional Neural Networks
• Research Proposal on Attention in reinforcement learning
• Research Proposal on Multi-objective optimization for deep learning hyperparameters
• Research Proposal on Multi-modal entity embeddings
• Research Proposal on Dynamic Graph Neural Networks
• Research Proposal on Image Captioning with Visual and Language Context
• Research Proposal on Deep Learning for Portfolio Diversification and Optimization
• Research Proposal on Motion Compensation for Video Restoration using Deep Learning
• Research Proposal on Multi-modal deep learning for multi-view sequential data analysis
• Research Proposal on Deep learning for cancer diagnosis from medical images
• Research Proposal on Deep transfer learning for radiology image analysis
• Research Proposal on Deep learning for improved iris recognition
• Research Proposal on Processing high-velocity and high-volume data streams
• Research Proposal on Causal inference for multi-class classification
• Research Proposal on Deep Extreme Learning Machines
• Research Proposal on Meta-representation learning
• Research Proposal on Data augmentation in Neural Machine Translation
• Research Proposal on Fairness and Bias in Health Record Analysis
• Research Proposal on Multi-omics Integration for Personalized Medicine
• Research Proposal on Deep Learning for Micro-expression Recognition
• Research Proposal on Deep Learning for Compressed Sensing in Compressed Speech
• Research Proposal on Transfer learning for feature engineering
• Research Proposal in Sentiment analysis on multimodal data
• Research Proposal on Online Extreme Learning Machines
• Research Proposal on Deep Learning for Face Anti-spoofing
• Research Proposal on Domain adaptation and transfer learning for multi-class classification
• Research Proposal on Reinforcement Learning for Natural Language Processing
• Research Proposal on Transfer Learning for Word Embeddings
• Research Proposal on Multi-head attention
• Research Proposal on Model-agnostic interpretation methods
• Research Proposal on Deep Generative Models for Microscopy Image Synthesis
• Research Proposal on Deep learning for quality assurance in radiotherapy
• Research Proposal on Low-light image super-resolution with deep learning
• Research Proposal on Fine-tuning Pre-trained Transformer Models for Image Captioning
• Research Proposal on Deep Learning for Facial Expression Recognition in the Wild
• Research Proposal on Multi-modal semantic analysis
• Research Proposal on Deep learning for gait recognition
• Research Proposal on Graph Reinforcement Learning
• Research Proposal on Gradient-based optimization for deep learning hyperparameters
• Research Proposal on Object detection with transformers
• Research Proposal on Transfer learning for multimedia classification
• Research Proposal on Generative adversarial networks for representation learning
• Research Proposal on Representation Learning with Graphs for Word Embeddings
• Research Proposal on Deep Learning for Motion Anomaly Detection in Videos
• Research Proposal on Deep Reinforcement Learning for Face Recognition
• Research Proposal on Deep Learning for Microscopy Image Restoration and Denoising
• Research Proposal on Deep Reinforcement Learning for Text Summarization
• Research Proposal on Deep learning for medical image registration
• Research Proposal on Improved computer-aided diagnosis in radiology with deep learning
• Research Proposal on Multi-modal cancer diagnosis using deep learning
• Research Proposal on Improved single image super-resolution using deep learning
• Research Proposal on Image Captioning in the Wild
• Research Proposal on Graph Convolutional Networks
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• Research Proposal on Real-time image super-resolution using deep learning
• Research Proposal on Deep learning for improved image quality assessment in radiology
• Research Proposal on Quantum reinforcement learning
• Research Proposal on Adaptive attention
• Research Proposal on Transfer Ensemble Learning
• Research Proposal on Multi-Task and Multi-Modal Learning with Convolutional Neural Networks
• Research Proposal on Two-stage object detection using Faster R-CNN
• Research Proposal on Face Attribute Prediction and Analysis
• Research Proposal on Deep learning for medical image synthesis and augmentation
• Research Proposal on Weather Forecasting for Agriculture using Deep Learning
• Research Proposal on Deep Learning for Video Compression and Restoration
• Research Proposal on Non-Parametric Topic Modeling
• Research Proposal on Deep Learning for Demand Forecasting in Supply Chain Management
• Research Proposal on Soil Moisture Prediction using Deep Learning
• Research Proposal on Deep Learning for Predictive Portfolio Management
• Research Proposal on Plant Disease Image Analysis with Deep Learning
• Research Proposal on Inventory Optimization with Deep Learning
• Research Proposal on Attention-based Image Denoising
• Research Proposal on Deep Generative Models for Drug Repurposing
• Research Proposal on Deep Learning for Compressed Sensing in Compressed Video
• Research Proposal on Transfer Learning for Topic Modeling
• Research Proposal on Representation learning for graph-structured data
• Research Proposal on Federated Learning for Recommendation System
• Research Proposal on Adversarial Ensemble Learning
• Research Proposal on Graph-based Natural Language Processing
• Research Proposal on Cross-domain sentiment analysis
• Research Proposal on Unsupervised feature learning using Belief Networks
• Research Proposal on Quantum neural networks
• Research Proposal on Representation learning for speech data
• Research Proposal on Object detection with semantic segmentation
• Research Proposal on Zero-shot Neural Machine Translation
• Research Proposal on Dialogue State Tracking
• Research Proposal on Image Captioning with Semantic Segmentation
• Research Proposal on Deep Learning for Image Registration and Stitching
• Research Proposal on Text Summarization with Sentiment Analysis
• Research Proposal on Deep learning for radiation therapy-related toxicity prediction
• Research Proposal on Improved image quality assessment in medical imaging using deep learning
• Research Proposal on Scene synthesis and manipulation using neural rendering
• Research Proposal on Multi-modal biometric recognition using deep learning
• Research Proposal on Named entity recognition for multi-modal data
• Research Proposal on Improved lung cancer diagnosis using deep learning
• Research Proposal on Multi-view sequential data analysis in low-resource settings using deep learning
• Research Proposal on Deep Learning for Video Denoising
• Research Proposal on Multi-agent Reinforcement Learning for Dynamic Environments
• Research Proposal on Deep Learning for Quality Control in Supply Chain
• Research Proposal on Object detection with domain adaptation
• Research Proposal on Plant Disease Segmentation and Recognition with Deep Learning
• Research Proposal on Adversarial Attacks and Defences in Face Recognition
• Research Proposal on Anomaly Detection in Videos with Deep Reinforcement Learning
• Research Proposal on Integrating Electronic Health Records and Genomics for Personalized Medicine
• Research Proposal on Adversarial attacks and defenses in radiology using deep learning
• Research Proposal on Deep Generative Models for Synthetic Facial Expression Data
• Research Proposal on Transfer Learning for Text Summarization
• Research Proposal on Extractive Text Summarization
• Research Proposal on Multi-modal Face Recognition
• Research Proposal on Multi-frame super-resolution using deep learning
• Research Proposal on Spatial-Temporal Graph Convolutional Networks
• Research Proposal on Real-time neural rendering for interactive environments
• Research Proposal on Convolutional Neural Networks for Object Detection and Segmentation
• Research Proposal on Transfer learning for named entity recognition
• Research Proposal on Transfer Learning for Semi-Supervised Learning
• Research Proposal on Deep learning for early cancer detection
• Research Proposal on Imitation Learning and Inverse Reinforcement Learning
• Research Proposal on Deep reinforcement learning for multi-view sequential data analysis
• Research Proposal on Attention for sequential reasoning
• Research Proposal on Deep learning for drug repurposing and de-novo drug discovery
• Research Proposal on Generative adversarial networks for domain adaptation
• Research Proposal on Crop Growth Monitoring using Deep Learning
• Research Proposal in Opinion mining on social media
• Research Proposal on Deep Learning for Video Frame Interpolation
• Research Proposal on Multi-lingual entity embeddings
• Research Proposal on Multi-agent Reinforcement Learning with Communication
• Research Proposal on Semantic augmentation
• Research Proposal on Deep Learning for Supplier Selection in Supply Chain
• Research Proposal on Domain adaptation in Neural Machine Translation
• Research Proposal on Deep Learning for Plant Leaf Disease Diagnosis
• Research Proposal on Multi-Turn conversational Dialogue Systems
• Research Proposal on Attention-based Multimodal Representation Learning
• Research Proposal on Deep Generative Models for Face Synthesis
• Research Proposal on Fine-grained Plant Disease Recognition with Deep Learning
• Research Proposal on Deep Reinforcement Learning for Drug Discovery
• Research Proposal on Deep Learning for Compressed Sensing in Medical Imaging
• Research Proposal on Transfer Learning for Microscopy Image Analysis
• Research Proposal on Multi-object Anomaly Detection in Videos with Deep Learning
• Research Proposal on Adversarial Training for Text Summarization
• Research Proposal on Human-in-the-loop Active Learning
• Research Proposal on Contextual word embeddings for semantic analysis
• Research Proposal on Scalable Neural Architecture Search for large-scale datasets and hardware accelerators
• Research Proposal on Deep learning for super-resolution of microscopy images
• Research Proposal on Causal inference and causal feature engineering
• Research Proposal on Improved 3D object rendering using deep neural networks
• Research Proposal on Convolutional Neural Networks (CNN) for Computer Vision tasks
• Research Proposal on Deep transfer learning for bioinformatics analysis
• Research Proposal on Self-training and Co-training for Semi-Supervised Learning
• Research Proposal on Video Super-resolution using Deep Learning
• Research Proposal on Meta-Learning for Few-shot Semi-Supervised Learning
• Research Proposal on Fertilizer Recommendation System using Deep Learning
• Research Proposal on Non-Linear Regression with Gaussian Processes
• Research Proposal on Adversarial Training for Image Denoising
• Research Proposal on Active and Semi-Supervised Ensemble Learning
• Research Proposal on Improved blood pressure prediction in cardiovascular disease patients using deep learning
• Research Proposal on Privacy-preserving Natural Language Processing
• Research Proposal on Named entity disambiguation using deep learning
• Research Proposal on Continuous Learning and Adaptation for Word Embeddings
• Research Proposal on Deep reinforcement learning in medical imaging and radiology
• Research Proposal on Incremental and online machine learning for data streams
• Research Proposal on Adversarial training for image super-resolution
• Research Proposal on Attention in federated learning
• Research Proposal on Multi-modal Microscopy Image Analysis
• Research Proposal Topic on Attention Mechanism for Natural Language Processing with Deep Learning
• Research Proposal on Mode collapse and stability in generative adversarial networks
• Research Proposal on Image Captioning with Scene Graphs
• Research Proposal Topics on Convolutional Neural Networks Research Challenges and Future Impacts
• Research Proposal on Adversarial attacks and defenses in sentiment analysis
• Research Proposal on Multi-person Motion Analysis
• Research Proposal on Graph Neural Network for Graph Analytics
• Research Proposal on Sentiment polarity detection
• Research Proposal on Transformer-based Neural Machine Translation
• Research Proposal on Deep Reinforcement Learning Methods for Active Decision Making
• Research Proposal on Cross-modal correspondence learning
• Research Proposal on Graph Transformer Networks
• Research Proposal on Deep Learning based Medical Imaging
• Research Proposal on Representation learning for pattern recognition
• Research Proposal on Mixup and cutmix data augmentation
• Research Proposal On Pre-trained Word embedding for Language models
• Research Proposal on Multi-modal data analysis using Belief Networks
• Research Proposal on Object detection with instance segmentation
• Research Proposal on Medical Machine Learning for Healthcare Analysis
• Research Proposal on Multi-modal data analysis using Extreme Learning Machines
• Research Proposal on Graph-based entity embeddings
• Research Proposal on Generative Adversarial Network
• Research Proposal on Quantum clustering algorithms
• Research Proposal on Sentiment analysis for low-resource languages
• Research Proposal on Hyperparameter Optimization and Fine-Tuning in Deep Neural Network
• Research Proposal on Transfer learning for hyperparameter optimization
• Research Proposal on Self-attention for sequential data
• Research Proposal on Deep Learning Models for Epilepsy Detection
• Research Proposal on Geometrical transformations for data augmentation
• Research Proposal on Meta-Learning for Word Embeddings
• Research Proposal Topics on Deep Learning Models for Epilepsy Detection
• Research Proposal on Anchor-free object detection
• Research Proposal on Multi-Task and Multi-lingual Natural Language Processing
• Research Proposal on Machine Learning in Alzheimer-s Disease Detection
• Research Proposal on Graph Autoencoders
• Research Proposal on Graph-based Semi-Supervised Learning
• Research Proposal on Machine Learning in Cancer Diagnosis
• Research Proposal on Human Pose Estimation
• Research Proposal on Adversarial Reinforcement Learning
• Research Proposal on Machine Learning in Covid-19 Diagnosis
• Research Proposal on Medication Recommendation
• Research Proposal in Light-weight and Efficient Convolutional Neural Networks for deployment on edge devices
• Research Proposal on Machine Learning in Diagnosis of Diabetes
• Research Proposal on Cross-age Face Recognition
• Research Proposal on Adversarial robustness in pattern recognition
• Research Proposal on Machine Learning in Heart Disease Diagnosis
• Research Proposal on Image Captioning with Transfer Learning
• Research Proposal on Representation learning for time series data
• Research Proposal on Machine Learning in Parkinson-s Diagnosis
• Research Proposal on Deep Learning for Toxicology and Safety Assessment
• Research Proposal on Instance Segmentation using Mask R-CNN
• Research Proposal on Deep Learning Models for Epileptic Focus Localization
• Research Proposal on Adversarial Training for Robust Microscopy Image Analysis
• Research Proposal on Dialogue Generation using Generative Models
• Research Proposal on Preprocessing Methods for Epilepsy Detection
• Research Proposal on Neural Text Summarization
• Research Proposal on Convolutional Deep Belief Networks
• Research Proposal on Human-in-the-loop Deep Reinforcement Learning
• Research Proposal on Multi-modal data analysis for multimedia classification
• Research Proposal on Interactive Machine Learning with Human Feedback
• Research Proposal on Online and Stream-based Regression
• Research Proposal on Deep Learning for Compressed Sensing in Image and Video Processing
• Research Proposal on Multi-view and multi-modal fusion for multi-class classification
• Research Proposal on Deep Learning for Risk Management in Portfolio Optimization
• Research Proposal on Sparse and Low-Rank Regression
• Research Proposal on Epilepsy Prediction
• Research Proposal on Deep Learning for Early Disease Detection in Plants
• Research Proposal on Compressed Sensing with Deep Autoencoders
• Research Proposal on Deep Learning for Multi-modal Representation in Healthcare
• Research Proposal on Deep Learning for Algorithmic Trading and Portfolio Optimization
• Research Proposal on Deep Learning for Predictive Sourcing in Supply Chain Management
• Research Proposal on Cross-modal Representation Learning with Deep Learning
• Research Proposal on Multi-agent Reinforcement Learning for Resource Allocation
• Research Proposal on Cooperative Multi-agent Reinforcement Learning
• Research Proposal on Plant Leaf Segmentation and Recognition with Deep Learning
• Research Proposal on Multi-topic Modeling with Deep Learning
• Research Proposal on Deep Learning for Topic Modeling
• Research Proposal on Supply Chain Risk Management with Deep Learning
• Research Proposal on Video Color Correction with Deep Learning
• Research Proposal on Fine-grained Plant Leaf Recognition with Deep Learning
• Research Proposal on Self-Supervised Image Denoising
• Research Proposal on Multi-class Plant Disease Recognition with Deep Learning
• Research Proposal on Deep Learning for Pest and Disease Detection in Crops
• Research Proposal on Topic Modeling with Graph-based Approaches
• Research Proposal on Video Restoration with Generative Adversarial Networks
• Research Proposal on Precision Irrigation Scheduling with Deep Learning
• Research Proposal on Deep learning for improved representation of multi-view sequential data
• Research Proposal on Deep learning for predicting drug efficacy and toxicity
• Research Proposal on Improved analysis of large-scale genomics data with deep learning
• Research Proposal on Deep learning for summarization and visualization of multi-view sequential data
• Research Proposal on Personalized cancer diagnosis using deep learning
• Research Proposal on Deep transfer learning for cancer diagnosis
• Research Proposal on Improved biometric recognition in low-resource settings using deep learning
• Research Proposal on Deep learning for improved facial recognition
• Research Proposal on Improved voice recognition with deep learning
• Research Proposal on Deep learning for patient-specific dose modeling
• Research Proposal on Neural rendering for product visualization in e-commerce
• Research Proposal on Deep learning for computer-aided diagnosis
• Research Proposal on Deep learning for improved medical image interpretation in radiology
• Research Proposal on Summarization with Pre-trained Language Models
• Research Proposal on Image super-resolution with attention mechanisms in deep learning
• Research Proposal on Deep Learning for Cross-cultural Facial Expression Recognition
• Research Proposal on Deep learning for dose prediction in radiotherapy
• Research Proposal on Deep Learning for Drug-Drug Interaction Prediction
• Research Proposal on Multi-modality medical image analysis with deep learning
• Research Proposal on Adversarial Training for Fair and Robust Drug Response Prediction
• Research Proposal on Improved segmentation of anatomical structures in radiotherapy using deep learning
• Research Proposal on Transfer Learning for Facial Expression Recognition
• Research Proposal on Abstractive Text Summarization
• Research Proposal on Deep Learning for Livestock Health Monitoring
• Research Proposal on Adversarial Training for Robust Facial Expression Recognition
• Research Proposal on Multi-class Plant Leaf Recognition with Deep Learning
• Research Proposal on Deep Learning for Object Detection and Classification in Microscopy Images
• Research Proposal on Multi-modal Representation Learning for Image and Text
• Research Proposal on Transfer Learning for Drug Response Prediction
• Research Proposal on Deep Learning for Event Detection in Video Surveillance
• Research Proposal on Time Series Data Analysis
• Research Proposal on Face Detection and Landmark Localization
• Research Proposal on Human-in-the-loop Anomaly Detection
• Research Proposal on Machine Learning for Pattern Recognition
• Research Proposal on Cross-Lingual Dialogue Systems
• Research Proposal on Deep Learning for Facial Action Unit Detection
• Research Proposal on Regression Model for Machine Learning
• Research Proposal on Medical Concept Embedding
• Research Proposal on Semantic parsing and question answering
• Research Proposal on Deep learning Algorithms and Recent advancements
• Research proposal on Natural Language Processing using Deep Learning
• Research Proposal on Predictive Analytics to forecast future outcomes
• Research proposal on Deep Learning-based Contextual Word Embedding for Text Generation
• Research Proposal on Discourse Representation-Aware Text Generation using Deep Learning Model
• Research Proposal on Deep Autoencoder based Text Generation for Natural Language
• Research Proposal on Reinforcement Learning
• Research Proposal Topics on Conversational Recommendation Systems
• Research Proposal on Pre-trained Deep Learning Model based Text Generation
• Research Proposal on Text Sequence Generation with Deep Transfer Learning
• Research Proposal in Modeling Deep Semi-Supervised Learning for Non-Redundant Text Generation
• Research Proposal in Utterances and Emoticons based Multi-Class Emotion Recognition
• Research Proposal in Negation Handling with Contextual Representation for Sentiment Classification
• Research Proposal on Deep Learning-based Emotion Classification
• Research Proposal in Sentiment Classification in Social Media with Deep Contextual Embedding
• Research Proposal in Deep Learning-based Emotion Classification in Conversational Text
• Research Proposal on Attention Mechanism-based Argument Mining using Deep Neural Network
• Research Proposal in Adaptive Deep Learning with Topic Extraction for Argument Mining
• Research Proposal on Context-aware Argument Mining with Deep Semi-supervised Learning
• Research Proposal in Deep Transfer Learning-based Sequential Keyphrase Generation
• Research Proposal on Deep Bi-directional Text Analysis for Sarcasm Detection
• Research Proposal in Emotion Transition Recognition with Contextual Embedding in Sarcasm Detection
• Research Topic on Attention-based Sarcasm Detection with Psycholinguistic Sources
• Research Proposal in Deep Attentive Model based Irony Text and Sarcasm Detection
• Research Proposal Topic on Discourse Structure and Opinion based Argumentation Mining
• Research Proposal in Sarcasm Detection using Syntactic and Semantic Feature Representation
• Research Proposal in Multi-Class Behavior Modeling in Deep Learning-based Sarcasm Detection
• Research Proposal on Deep Transfer Learning for Irony Detection
• Research Proposal in Deep Neural Network-based Sarcasm Detection with Multi-Task Learning
• Research Proposal in Deep Learning-Guided Credible User Identification using Social Network Structure and User-Generated Content
• Research Proposal in Semi-supervised Misinformation Detection in Social Network
• Research Proposal in Deep Contextualized Word Representation for Fake News Classification
• Research Proposal on Self-Attentive Network-based Rumour Classification in Social Media
• Research Proposal in Multi-Modal Rumour Classification with Deep Ensemble Learning
• Research Proposal in Hybrid Deep Learning Model based Fake News Detection in Social Network
• Research Proposal on Anomaly Detection by Applying the Machine Learning Technique
• Research Proposal on Transformer based Opinion Mining Approach for Fake News Detection
• Research Proposal in Data Augmentation for Deep Learning-based Plant Disease Detection
• Research Proposal in Multi-Class Imbalance Handling with Deep Learning in Plant Disease Detection
• Research Proposal on Incremental Learning-based Concept Drift Detection in Stream Classification
• Research Proposal in Class-Incremental Learning for Large-Scale IoT Prediction
• Research Proposal in Time-series Forecasting using Weighted Incremental Learning
• Research Proposal on Deep Reinforcement Learning based Time Series Prediction
• Research Proposal in Federated Learning for Intelligent IoT Healthcare System
• Research Proposal on Deep Learning based Stream Data Imputation for IoT Applications
• Research Proposal on Deep Incremental Learning-based Cyber Security Threats Prediction
• Research Proposal in Aspect based Opinion Mining for Personalized Recommendation
• Research Proposal in Personalized Recommendation with Contextual Pre-Filtering
• Research Proposal in Temporal and Spatial Context-based Group Recommendation
• Research Proposal on Session based Recommender System with Representation learning
• Research Proposal in Serendipity-aware Product Recommendation
• Research Proposal in Deep Preference Prediction for Novelty and Diversity-Aware Top-N Recommendation
• Research Proposal on Personalized Recommendation with Neural Attention Model
• Research Proposal in Cross-domain Depression Detection in Social Media
• Research Proposal in Emotional Feature Extraction in Depression Detection
• Research Proposal in Contextual Recommendation with Deep Reinforcement Learning
• Research Proposal on Deep Neural Network-based Cross-Domain Recommendation
• Research Proposal in Multimodal Extraction for Depression Detection
• Research Proposal on Early Depression Detection with Deep Attention Network
• Research Proposal in Proactive Intrusion Detection in Cloud using Deep Reinforcement Learning
• Research Proposal in Context Vector Representation of Text Sequence for Depression Detection
• Research Proposal in Sparsity Handling in Recommender System with Transfer Learning
• Research Proposal on Artificial Intelligence and Lexicon based Suicide Attempt Prevention
• Research Proposal in Modeling Deep Neural Network for Mental Illness Detection from Healthcare Data
• Research Proposal in Deep Learning-based Domain Adaptation for Recommendation
• Research proposal on Emotion Classification using Deep Learning Models
• Research Proposal in Topic Modeling for Personalized Product Recommendation
• Research Proposal in Deep Reinforcement Learning based Resource Provisioning for Container-based Cloud Environment
• Research Proposal in Energy and Delay-Aware Scheduling with Deep Learning in Fog Computing
• Research Proposal in Spammer Detection in Social Network from the Advertiser Behavior Modeling
• Research Proposal in Social Information based People Recommendation
• Research Proposal in Deep Learning-based Advertiser Reliability Computation in Social Network
• Research Proposal in Artificial Neural Network-based Missing Value Imputation in Disease Detection
• Research Proposal in Deep Transfer Learning-based Disease Detection
• Research Proposal in Aspect based Depressive Feature Extraction for Multi-Class Depression Classification
• Research Proposal in Environmental Data-Aware Behavior Modeling and Depression Detection
• Research Proposal on User Credibility Detection in Social Networks using Deep Learning Models
• Research Proposal in Incremental Clustering Model for Malware Classification
• Research Proposal in Deep Feature Extraction with One-class Classification for Anomaly Detection
• Research Proposal in Fake News Dissemination Detection with Deep Neural Network
• Research Proposal in Deep Learning-based Prediction for Mobile Cloud Offloading Decision-Making
• Research Proposal in Hybrid Deep Learning-based Traffic Congestion Prediction in VANET
• Research Proposal in Deep Ensemble Learning-based Android Malware Detection
• Research Proposal in Proactive Prediction of Insider Threats using Deep Neural Network
• Research Proposal in Delay-constrained Computation Offloading with Deep Learning in Fog Environment
• Research Proposal in Latency-aware IoT Service Placement in Fog through Deep Learning Prediction
• Research Proposal in Proactive Management of Edge Resources with Federated Learning
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• Research Proposal in Energy-efficient Edge Computation with Light-weight Deep Learning Model
• Research Proposal Topic on Integrating Deep Learning with Rule-based Model for Sarcasm Detection
• Research Proposal in Deep Incremental Learning based Intrusion Detection in IoT Environment
• Research Proposal for Deep Learning based Explainable Recommendation Systems
• Research Proposal in Federated Learning-based Cooperative Management of Fog Computation Resources
• Research Proposal in Mobility-aware Collaborative Resource Management in Edge Intelligence System
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• Research Proposal on Zero-day Attack Detection using Deep Neural Network
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• Research Proposal in Anomaly-aware Cloud Security Model with Deep Transfer Learning
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A guide to improve your research proposals.

dair-ai/awesome-research-proposals-guide

Folders and files, repository files navigation, guide to awesome research proposals.

For the last few weeks, I have seen many principal investigators (PIs) and professors announcing positions in the search for new graduate students. However, very few offer suggestions on what to include in the application, research proposal, or statement of purpose. In my Ph.D., not only did I get to work with different research teams, but I was also in charge of writing research proposals that ranged from fellowship applications to applications for industry and government grants. I would like to share a few tips and suggestions on how to improve your research proposal for those seeking to apply to graduate school, specifically those with machine learning backgrounds. The suggestions here can easily be adopted to improve proposals for fellowships, grad school, grants, scholarships, etc. I don’t claim that following my advice here will guarantee success. However, in my years of experience writing proposals and being a researcher, the following components really helped me prepare successful and strong research proposals.

Introduction

Like with anything you are writing, that first paragraph of your research proposal or statement of purpose should be clear and concise. Focus on making it about what your experience is and what topics you are specifically interested to investigate. You should seek to answer what is your topic of interest? Why you are interested in this topic (motivations should be clear and straight to the point), and a brief explanation of how you are thinking about approaching a future problem. It’s important to keep the introduction short and concise and let it serve as a high-level overview of what you are about to discuss in the proposal. Each of the sections that follow provides some guidance on how to strengthen the proposal, including what to include and what to avoid. It’s not an example of how to write the proposal but a recipe for strengthening and improving it.

Before thinking about the main points you are going to include in your proposal, think about the scope. There are so many things you can write about but you only have 1 or 2 pages to really make a strong case for why you are a strong candidate for the lab you are applying to. As you write your proposal you should think about a theme and how to keep everything concise. Defining a scope early on helps to focus on the important details you want to include in your proposal. Prepare a checklist of the most important points that should use as a guide for writing a strong proposal.

Be very specific about the topics and problems you are interested in. For instance, just saying that you are interested in robust machine learning may not be specific enough. Maybe state that you are particularly interested in understanding the linguistic knowledge learned by BERT models (this is a very rough example). The more specific you are the better. It shows that you have experience and an ability to scope the research. Along with this, mention a few of the research questions you are thinking about for the work you plan to conduct. These could be very rough research questions but they help to give the reviewer an indication that you are already thinking ahead about the work you will be conducting. That can only be a good sign for the reviewer.

You could be interested or are working on multiple topics which is not rare in machine learning research. Your job is to write a proposal in a coherent way, making sure to focus on an overarching theme. When researchers work on many different topics it could give a sign that they are not focused enough as a researcher. It could also show a lack of experience as well. Seasoned researchers are really good at providing a reasonable scope for their research projects and that’s quickly visible in the proposal. The scope and theme help to keep the proposals tidy and coherent.

Background refers to your professional experience, the background of the work you have done, and the one you intend to work on. You must be able to share your research experience, current background, and the precise topics you are highly motivated to investigate. This is typically easy to write about and you should already be an expert on this by now. In fact, as you go through this guide, you will see that I already assume you have a rough draft prepared. The hardest part is to find a theme that connects with the reviewer. You should definitely try to conduct a bit more research about the labs you are applying to and try to find themes that make sense to include. In some cases, PIs are interested in expanding the scope of the research lab and that’s where your unique expertise may be useful. Really, you should try different ways to showcase your work ethic, research goals, and resiliency as a researcher. The following important questions may pop up as you prepare your background and discuss your experience:

How important is my publication record? I see this question a lot. You are worried about the amount of publication you have affecting your chances to get accepted into your dream research lab. In my opinion, if the PI you are applying to cares too much about the number of publications it’s probably not a good idea to join this lab in the first place. I think this is just a myth. The number of publications alone doesn’t really say much about the quality of the researcher that you are and can be. With that said, I believe the number of publications is not so important if you are able to convince the reviewer that you are a keen learner and can go deep into a topic, including sharing your experience on how you overcome challenges along the way. The amount of publications is not the only indicator that reviewers are looking at. If you have other types of experiences like industry or teaching experience make sure to include and highlight those as well. Research experience can be demonstrated in many different ways not only through a high number of publications. If you break down what research consists of you will see tasks like team management, project management, experimentation, visualization, data processing, writing, reviewing, etc. These are all important to make a research project successful. Keep that in mind as you prepare your proposal and highlight the tasks that you are good at. There is always an opportunity to grow in other areas, that is expected.

What if the work of the research lab is unrelated to what you are currently studying or the theme you are focusing on? It could be the case that you are applying to a research lab that is doing work you are interested to investigate but are currently not involved in. Like I said earlier, there is still hope. Talk about your experience as a researcher and make it clear how you will be able to contribute. You may not be familiar with how to apply model X for topic Y but perhaps you have a lot of experience working with similar data Z which the lab is working with. There is usually something you can use to connect with the work the lab is conducting.

I don’t have much to say about the topic of “theme” here. The reason I include it as a separate section is to remind you of how important it is to find a theme for your proposal. You may be tempted to write about all the little details of your experience with the idea to impress the reviewer of your experience. However, in this case, typically, more means less. Determining the scope, your focus, and the specific topics you are interested in working on are more important. Less is more, but ensure you have compacted all the important stuff into a strong overarching theme. As an example, in my case, I worked on many different applied machine learning research projects in the context of social computing. I always focused on affective computing as a theme when writing proposals. This is my expertise and I always felt more comfortable writing about it. That’s the point. You should feel comfortable about what you are writing, otherwise, it will easily show in your proposal and you don’t want that.

Contributions

As researchers, we are always tempted to talk about “our” work as the work you have conducted in the different teams and projects you have collaborated on. There is nothing wrong with that. However, a research proposal is about what “you” have done specifically. Think about it. It’s really hard to convince others, through writing, about what exactly your expertise is. So try to focus on specific contributions you have made to the different research projects you have worked in. Write extensively about your contributions to the research projects not the contributions of others. We can’t all be good at everything. It’s important to focus on your strengths as a researcher and use that as a theme for the proposal. Remember, for this one time, it’s all about what you are capable of, not the team. It’s your time to shine.

Methodologies

Besides the background and the work you have done in the past, you are expected to provide more details about the topics you are interested to work on. This goes back to my earlier point about showcasing your experience. Seasoned researchers are typically good at writing about ideas and potential methodologies they plan to use. Talk briefly about the problem, the data collection strategies, the methods you propose, and the types of experiments you will be conducting. It needs not to be something definitive, it just must show rigor, readiness, and expertise. In fact, in most cases, topics evolve or change over time. It could just end up that you work on something completely different from what you proposed initially. Don’t worry about that for now. Just take the chance to emphasize your expertise and hands-on abilities.

Timeline/Project Management

Most people don’t talk about this in academia, perhaps because time is a very delicate topic. However, timing is everything. With so many deadlines, course work, and life itself, a graduate student must possess excellent project and time management skills. You should know exactly how much time a set of experiments might take and mention that in the proposal. Give rough estimates because at the end of the day things change as I mentioned earlier. Your ability to manage the project and timeline of it will make you stand out as a researcher. Timing is critical for both the student and the PI.

Besides time, you can also try to include the different components involving the work you are proposing to work on. You don’t have a lot of room to express this in a 2-pages proposal but you can do your best to provide rough estimates to give the reviewer the confidence that you are thinking ahead and are aware of all these important things. This shows maturity and experience.

If there is room, slightly mention what could cause delays or could potentially be a constraint for the project so that the PI knows what to expect and plan ahead. For instance, if your work might requires multiple GPUs to run experiments for two months then those details are very important to include in the proposal. Just keep it very short and don’t get into too many details. But this is a good chance to showcase your awareness and project management skills.

Future Goals and Objectives

You don’t need a future work section in a research proposal but I always considered including extra bits of information about myself that helped strengthen my application. In the past, I realized that being a researcher requires not only hard work but the ability to connect with the team and keep everyone motivated and encouraged. One way I kept research teams motivated is by discussing with them their future goals and objectives. Keeping that at the center of the discussion always allowed us to reflect on the importance of what we were working on. Displaying this in a proposal is not mandatory but if you have space in your proposal express what you ideally would love to get out of the experience. Are you planning for a postdoc or an industrial career? Sharing these goals shows that you are ambitious and that you are determined to succeed in the work you will conduct. Again, it’s all about showcasing the desire to grow.

Although the tools you will use don’t really matter that much at this point, it’s important to realize that besides your own work, you will be working in research teams in a lot of cases. Conduct a quick search on the projects published by the lab you are interested to join and make sure you are familiar or aware of the tools used by those researchers you might be working with. If you are not familiar with the tools, it shouldn’t matter that much but you should try to express in the proposal that you are willing to learn those frameworks and that you can do so easily given some timeframe. It’s all about making the reviewer feel confident about your expertise and ability to adapt. From what I have seen, integrity, maturity, and eagerness to learn are undoubtedly the best qualities of a seasoned researcher.

That’s all I have for you today. I believe that if you pay close attention to the points I discussed in this guide and apply them to your proposal it could give you an edge and an opportunity to join that dream research lab. I am sure that I didn’t cover a lot of other topics and questions that you may have. If that is the case, open an issue, and I will address them and continue refining the guide.

If you wish to hear more about my advice and tips, including different ML-related guides and topics, connect with me on Twitter or follow my blog .

How you can contribute to this guide?

• The guide is basically in draft mode. If you have feedback or grammar corrections please let me know by opening an issue.
• Add more components that in your experience helped research proposals stand out
• It will be great to add more examples to each section

A Proposal on Machine Learning via Dynamical Systems

• Published: 22 March 2017
• Volume 5 , pages 1–11, ( 2017 )

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We discuss the idea of using continuous dynamical systems to model general high-dimensional nonlinear functions used in machine learning. We also discuss the connection with deep learning.

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Acknowledgements

This is part of an ongoing project with several collaborators, including Jiequn Han, Qianxiao Li, Jianfeng Lu and Cheng Tai. The author benefitted a great deal from discussions with them, particularly Jiequn Han. This work is supported in part by the Major Program of NNSFC under Grant 91130005, ONR N00014-13-1-0338 and DOE DE-SC0009248.

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Dedicated to Professor Chi-Wang Shu on the occasion of his 60th birthday.

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E, W. A Proposal on Machine Learning via Dynamical Systems. Commun. Math. Stat. 5 , 1–11 (2017). https://doi.org/10.1007/s40304-017-0103-z

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This quarter in CS230, you will learn about a wide range of deep learning applications. Part of the learning will be online, during in-class lectures and when completing assignments, but you will really experience hands-on work in your final project. We would like you to choose wisely a project that fits your interests. One that would be both motivating and technically challenging.

Most students do one of three kinds of projects:

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Many fantastic class projects come from students picking either an application area that they’re interested in, or picking some subfield of machine learning that they want to explore more. So, pick something that you can get excited and passionate about! Be brave rather than timid, and do feel free to propose ambitious things that you’re excited about. (Just be sure to ask us for help if you’re uncertain how to best get started.) Alternatively, if you’re already working on a research or industry project that deep learning might apply to, then you may already have a great project idea.

Project Hints

A very good CS230 project will be a publishable or nearly-publishable piece of work. Each year, some number of students continue working on their projects after completing CS230, submitting their work to a conferences or journals. Thus, for inspiration, you might also look at some recent deep learning research papers. Two of the main machine learning conferences are ICML and NeurIPS . You may also want to look at class projects from previous years of CS230 ( Fall 2017 , Winter 2018 , Spring 2018 , Fall 2018 ) and other machine learning/deep learning classes ( CS229 , CS229A , CS221 , CS224N , CS231N ) is a good way to get ideas. Finally, we crowdsourced and curated a list of ideas that you can view here , and an older one here , and a (requires Stanford login).

Once you have identified a topic of interest, it can be useful to look up existing research on relevant topics by searching related keywords on an academic search engine such as: http://scholar.google.com . Another important aspect of designing your project is to identify one or several datasets suitable for your topic of interest. If that data needs considerable pre-processing to suit your task, or that you intend to collect the needed data yourself, keep in mind that this is only one part of the expected project work, but can often take considerable time. We still expect a solid methodology and discussion of results, so pace your project accordingly.

Notes on a few specific types of projects:

• Computation power. Amazon Web Services is sponsoring the CS230 projects by providing you with GPU credits to run your experiments! We will update regarding how to retrieve your GPU credits. Alternatively Google Cloud and Microsoft Azure offer free academic units which you can apply to.
• Preprocessed datasets. While we don’t want you to have to spend much time collecting raw data, the process of inspecting and visualizing the data, trying out different types of preprocessing, and doing error analysis is often an important part of machine learning. Hence if you choose to use preprepared datasets (e.g. from Kaggle, the UCI machine learning repository, etc.) we encourage you to do some data exploration and analysis to get familiar with the problem.
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Evaluation: We will not be disclosing the breakdown of the 40% that the final project is worth amongst the different parts, but the video and final report will combine to be the majority of the grade. Attendance and participation during your TA meetings will also be considered. Projects will be evaluated based on:

• The technical quality of the work. (I.e., Does the technical material make sense? Are the things tried reasonable? Are the proposed algorithms or applications clever and interesting? Do the authors convey novel insight about the problem and/or algorithms?)
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In order to highlight these components, it is important you present a solid discussion regarding the learnings from the development of your method, and summarizing how your work compares to existing approaches.

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First, make sure to submit the following Google form so that we can match you to a TA mentor. In the form you will have to provide your project title, team members and relevant research area(s).

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In the proposal, below your project title, include the project category. The category can be one of:

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Presenting pointers to one relevant dataset and one example of prior research on the topic are a valuable (optional) addition. We link one past example of a good project proposal here and a latex template .

Deadline: May 19, Friday 11:59 PM

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We recommend searching for conference presentation sessions (AAAI, Neurips, ECCV, ICML, ICLR etc) and following those formats.

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Machine Learning: Algorithms, Real-World Applications and Research Directions

Iqbal h. sarker.

1 Swinburne University of Technology, Melbourne, VIC 3122 Australia

2 Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, 4349 Chattogram, Bangladesh

In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated  applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning , which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

Introduction

We live in the age of data, where everything around us is connected to a data source, and everything in our lives is digitally recorded [ 21 , 103 ]. For instance, the current electronic world has a wealth of various kinds of data, such as the Internet of Things (IoT) data, cybersecurity data, smart city data, business data, smartphone data, social media data, health data, COVID-19 data, and many more. The data can be structured, semi-structured, or unstructured, discussed briefly in Sect. “ Types of Real-World Data and Machine Learning Techniques ”, which is increasing day-by-day. Extracting insights from these data can be used to build various intelligent applications in the relevant domains. For instance, to build a data-driven automated and intelligent cybersecurity system, the relevant cybersecurity data can be used [ 105 ]; to build personalized context-aware smart mobile applications, the relevant mobile data can be used [ 103 ], and so on. Thus, the data management tools and techniques having the capability of extracting insights or useful knowledge from the data in a timely and intelligent way is urgently needed, on which the real-world applications are based.

Artificial intelligence (AI), particularly, machine learning (ML) have grown rapidly in recent years in the context of data analysis and computing that typically allows the applications to function in an intelligent manner [ 95 ]. ML usually provides systems with the ability to learn and enhance from experience automatically without being specifically programmed and is generally referred to as the most popular latest technologies in the fourth industrial revolution (4 IR or Industry 4.0) [ 103 , 105 ]. “Industry 4.0” [ 114 ] is typically the ongoing automation of conventional manufacturing and industrial practices, including exploratory data processing, using new smart technologies such as machine learning automation. Thus, to intelligently analyze these data and to develop the corresponding real-world applications, machine learning algorithms is the key. The learning algorithms can be categorized into four major types, such as supervised, unsupervised, semi-supervised, and reinforcement learning in the area [ 75 ], discussed briefly in Sect. “ Types of Real-World Data and Machine Learning Techniques ”. The popularity of these approaches to learning is increasing day-by-day, which is shown in Fig. ​ Fig.1, 1 , based on data collected from Google Trends [ 4 ] over the last five years. The x - axis of the figure indicates the specific dates and the corresponding popularity score within the range of 0 ( m i n i m u m ) to 100 ( m a x i m u m ) has been shown in y - axis . According to Fig. ​ Fig.1, 1 , the popularity indication values for these learning types are low in 2015 and are increasing day by day. These statistics motivate us to study on machine learning in this paper, which can play an important role in the real-world through Industry 4.0 automation.

The worldwide popularity score of various types of ML algorithms (supervised, unsupervised, semi-supervised, and reinforcement) in a range of 0 (min) to 100 (max) over time where x-axis represents the timestamp information and y-axis represents the corresponding score

In general, the effectiveness and the efficiency of a machine learning solution depend on the nature and characteristics of data and the performance of the learning algorithms . In the area of machine learning algorithms, classification analysis, regression, data clustering, feature engineering and dimensionality reduction, association rule learning, or reinforcement learning techniques exist to effectively build data-driven systems [ 41 , 125 ]. Besides, deep learning originated from the artificial neural network that can be used to intelligently analyze data, which is known as part of a wider family of machine learning approaches [ 96 ]. Thus, selecting a proper learning algorithm that is suitable for the target application in a particular domain is challenging. The reason is that the purpose of different learning algorithms is different, even the outcome of different learning algorithms in a similar category may vary depending on the data characteristics [ 106 ]. Thus, it is important to understand the principles of various machine learning algorithms and their applicability to apply in various real-world application areas, such as IoT systems, cybersecurity services, business and recommendation systems, smart cities, healthcare and COVID-19, context-aware systems, sustainable agriculture, and many more that are explained briefly in Sect. “ Applications of Machine Learning ”.

Based on the importance and potentiality of “Machine Learning” to analyze the data mentioned above, in this paper, we provide a comprehensive view on various types of machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, the key contribution of this study is explaining the principles and potentiality of different machine learning techniques, and their applicability in various real-world application areas mentioned earlier. The purpose of this paper is, therefore, to provide a basic guide for those academia and industry people who want to study, research, and develop data-driven automated and intelligent systems in the relevant areas based on machine learning techniques.

The key contributions of this paper are listed as follows:

• To define the scope of our study by taking into account the nature and characteristics of various types of real-world data and the capabilities of various learning techniques.
• To provide a comprehensive view on machine learning algorithms that can be applied to enhance the intelligence and capabilities of a data-driven application.
• To discuss the applicability of machine learning-based solutions in various real-world application domains.
• To highlight and summarize the potential research directions within the scope of our study for intelligent data analysis and services.

The rest of the paper is organized as follows. The next section presents the types of data and machine learning algorithms in a broader sense and defines the scope of our study. We briefly discuss and explain different machine learning algorithms in the subsequent section followed by which various real-world application areas based on machine learning algorithms are discussed and summarized. In the penultimate section, we highlight several research issues and potential future directions, and the final section concludes this paper.

Types of Real-World Data and Machine Learning Techniques

Machine learning algorithms typically consume and process data to learn the related patterns about individuals, business processes, transactions, events, and so on. In the following, we discuss various types of real-world data as well as categories of machine learning algorithms.

Types of Real-World Data

Usually, the availability of data is considered as the key to construct a machine learning model or data-driven real-world systems [ 103 , 105 ]. Data can be of various forms, such as structured, semi-structured, or unstructured [ 41 , 72 ]. Besides, the “metadata” is another type that typically represents data about the data. In the following, we briefly discuss these types of data.

• Structured: It has a well-defined structure, conforms to a data model following a standard order, which is highly organized and easily accessed, and used by an entity or a computer program. In well-defined schemes, such as relational databases, structured data are typically stored, i.e., in a tabular format. For instance, names, dates, addresses, credit card numbers, stock information, geolocation, etc. are examples of structured data.
• Unstructured: On the other hand, there is no pre-defined format or organization for unstructured data, making it much more difficult to capture, process, and analyze, mostly containing text and multimedia material. For example, sensor data, emails, blog entries, wikis, and word processing documents, PDF files, audio files, videos, images, presentations, web pages, and many other types of business documents can be considered as unstructured data.
• Semi-structured: Semi-structured data are not stored in a relational database like the structured data mentioned above, but it does have certain organizational properties that make it easier to analyze. HTML, XML, JSON documents, NoSQL databases, etc., are some examples of semi-structured data.
• Metadata: It is not the normal form of data, but “data about data”. The primary difference between “data” and “metadata” is that data are simply the material that can classify, measure, or even document something relative to an organization’s data properties. On the other hand, metadata describes the relevant data information, giving it more significance for data users. A basic example of a document’s metadata might be the author, file size, date generated by the document, keywords to define the document, etc.

In the area of machine learning and data science, researchers use various widely used datasets for different purposes. These are, for example, cybersecurity datasets such as NSL-KDD [ 119 ], UNSW-NB15 [ 76 ], ISCX’12 [ 1 ], CIC-DDoS2019 [ 2 ], Bot-IoT [ 59 ], etc., smartphone datasets such as phone call logs [ 84 , 101 ], SMS Log [ 29 ], mobile application usages logs [ 137 ] [ 117 ], mobile phone notification logs [ 73 ] etc., IoT data [ 16 , 57 , 62 ], agriculture and e-commerce data [ 120 , 138 ], health data such as heart disease [ 92 ], diabetes mellitus [ 83 , 134 ], COVID-19 [ 43 , 74 ], etc., and many more in various application domains. The data can be in different types discussed above, which may vary from application to application in the real world. To analyze such data in a particular problem domain, and to extract the insights or useful knowledge from the data for building the real-world intelligent applications, different types of machine learning techniques can be used according to their learning capabilities, which is discussed in the following.

Types of Machine Learning Techniques

Machine Learning algorithms are mainly divided into four categories: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning [ 75 ], as shown in Fig. ​ Fig.2. 2 . In the following, we briefly discuss each type of learning technique with the scope of their applicability to solve real-world problems.

Various types of machine learning techniques

• Supervised: Supervised learning is typically the task of machine learning to learn a function that maps an input to an output based on sample input-output pairs [ 41 ]. It uses labeled training data and a collection of training examples to infer a function. Supervised learning is carried out when certain goals are identified to be accomplished from a certain set of inputs [ 105 ], i.e., a task-driven approach . The most common supervised tasks are “classification” that separates the data, and “regression” that fits the data. For instance, predicting the class label or sentiment of a piece of text, like a tweet or a product review, i.e., text classification, is an example of supervised learning.
• Unsupervised: Unsupervised learning analyzes unlabeled datasets without the need for human interference, i.e., a data-driven process [ 41 ]. This is widely used for extracting generative features, identifying meaningful trends and structures, groupings in results, and exploratory purposes. The most common unsupervised learning tasks are clustering, density estimation, feature learning, dimensionality reduction, finding association rules, anomaly detection, etc.
• Semi-supervised: Semi-supervised learning can be defined as a hybridization of the above-mentioned supervised and unsupervised methods, as it operates on both labeled and unlabeled data [ 41 , 105 ]. Thus, it falls between learning “without supervision” and learning “with supervision”. In the real world, labeled data could be rare in several contexts, and unlabeled data are numerous, where semi-supervised learning is useful [ 75 ]. The ultimate goal of a semi-supervised learning model is to provide a better outcome for prediction than that produced using the labeled data alone from the model. Some application areas where semi-supervised learning is used include machine translation, fraud detection, labeling data and text classification.
• Reinforcement: Reinforcement learning is a type of machine learning algorithm that enables software agents and machines to automatically evaluate the optimal behavior in a particular context or environment to improve its efficiency [ 52 ], i.e., an environment-driven approach . This type of learning is based on reward or penalty, and its ultimate goal is to use insights obtained from environmental activists to take action to increase the reward or minimize the risk [ 75 ]. It is a powerful tool for training AI models that can help increase automation or optimize the operational efficiency of sophisticated systems such as robotics, autonomous driving tasks, manufacturing and supply chain logistics, however, not preferable to use it for solving the basic or straightforward problems.

Thus, to build effective models in various application areas different types of machine learning techniques can play a significant role according to their learning capabilities, depending on the nature of the data discussed earlier, and the target outcome. In Table ​ Table1, 1 , we summarize various types of machine learning techniques with examples. In the following, we provide a comprehensive view of machine learning algorithms that can be applied to enhance the intelligence and capabilities of a data-driven application.

Various types of machine learning techniques with examples

Machine Learning Tasks and Algorithms

In this section, we discuss various machine learning algorithms that include classification analysis, regression analysis, data clustering, association rule learning, feature engineering for dimensionality reduction, as well as deep learning methods. A general structure of a machine learning-based predictive model has been shown in Fig. ​ Fig.3, 3 , where the model is trained from historical data in phase 1 and the outcome is generated in phase 2 for the new test data.

A general structure of a machine learning based predictive model considering both the training and testing phase

Classification Analysis

Classification is regarded as a supervised learning method in machine learning, referring to a problem of predictive modeling as well, where a class label is predicted for a given example [ 41 ]. Mathematically, it maps a function ( f ) from input variables ( X ) to output variables ( Y ) as target, label or categories. To predict the class of given data points, it can be carried out on structured or unstructured data. For example, spam detection such as “spam” and “not spam” in email service providers can be a classification problem. In the following, we summarize the common classification problems.

• Binary classification: It refers to the classification tasks having two class labels such as “true and false” or “yes and no” [ 41 ]. In such binary classification tasks, one class could be the normal state, while the abnormal state could be another class. For instance, “cancer not detected” is the normal state of a task that involves a medical test, and “cancer detected” could be considered as the abnormal state. Similarly, “spam” and “not spam” in the above example of email service providers are considered as binary classification.
• Multiclass classification: Traditionally, this refers to those classification tasks having more than two class labels [ 41 ]. The multiclass classification does not have the principle of normal and abnormal outcomes, unlike binary classification tasks. Instead, within a range of specified classes, examples are classified as belonging to one. For example, it can be a multiclass classification task to classify various types of network attacks in the NSL-KDD [ 119 ] dataset, where the attack categories are classified into four class labels, such as DoS (Denial of Service Attack), U2R (User to Root Attack), R2L (Root to Local Attack), and Probing Attack.
• Multi-label classification: In machine learning, multi-label classification is an important consideration where an example is associated with several classes or labels. Thus, it is a generalization of multiclass classification, where the classes involved in the problem are hierarchically structured, and each example may simultaneously belong to more than one class in each hierarchical level, e.g., multi-level text classification. For instance, Google news can be presented under the categories of a “city name”, “technology”, or “latest news”, etc. Multi-label classification includes advanced machine learning algorithms that support predicting various mutually non-exclusive classes or labels, unlike traditional classification tasks where class labels are mutually exclusive [ 82 ].

Many classification algorithms have been proposed in the machine learning and data science literature [ 41 , 125 ]. In the following, we summarize the most common and popular methods that are used widely in various application areas.

• Naive Bayes (NB): The naive Bayes algorithm is based on the Bayes’ theorem with the assumption of independence between each pair of features [ 51 ]. It works well and can be used for both binary and multi-class categories in many real-world situations, such as document or text classification, spam filtering, etc. To effectively classify the noisy instances in the data and to construct a robust prediction model, the NB classifier can be used [ 94 ]. The key benefit is that, compared to more sophisticated approaches, it needs a small amount of training data to estimate the necessary parameters and quickly [ 82 ]. However, its performance may affect due to its strong assumptions on features independence. Gaussian, Multinomial, Complement, Bernoulli, and Categorical are the common variants of NB classifier [ 82 ].
• Linear Discriminant Analysis (LDA): Linear Discriminant Analysis (LDA) is a linear decision boundary classifier created by fitting class conditional densities to data and applying Bayes’ rule [ 51 , 82 ]. This method is also known as a generalization of Fisher’s linear discriminant, which projects a given dataset into a lower-dimensional space, i.e., a reduction of dimensionality that minimizes the complexity of the model or reduces the resulting model’s computational costs. The standard LDA model usually suits each class with a Gaussian density, assuming that all classes share the same covariance matrix [ 82 ]. LDA is closely related to ANOVA (analysis of variance) and regression analysis, which seek to express one dependent variable as a linear combination of other features or measurements.
• Logistic regression (LR): Another common probabilistic based statistical model used to solve classification issues in machine learning is Logistic Regression (LR) [ 64 ]. Logistic regression typically uses a logistic function to estimate the probabilities, which is also referred to as the mathematically defined sigmoid function in Eq. 1 . It can overfit high-dimensional datasets and works well when the dataset can be separated linearly. The regularization (L1 and L2) techniques [ 82 ] can be used to avoid over-fitting in such scenarios. The assumption of linearity between the dependent and independent variables is considered as a major drawback of Logistic Regression. It can be used for both classification and regression problems, but it is more commonly used for classification. g ( z ) = 1 1 + exp ( - z ) . 1
• K-nearest neighbors (KNN): K-Nearest Neighbors (KNN) [ 9 ] is an “instance-based learning” or non-generalizing learning, also known as a “lazy learning” algorithm. It does not focus on constructing a general internal model; instead, it stores all instances corresponding to training data in n -dimensional space. KNN uses data and classifies new data points based on similarity measures (e.g., Euclidean distance function) [ 82 ]. Classification is computed from a simple majority vote of the k nearest neighbors of each point. It is quite robust to noisy training data, and accuracy depends on the data quality. The biggest issue with KNN is to choose the optimal number of neighbors to be considered. KNN can be used both for classification as well as regression.
• Support vector machine (SVM): In machine learning, another common technique that can be used for classification, regression, or other tasks is a support vector machine (SVM) [ 56 ]. In high- or infinite-dimensional space, a support vector machine constructs a hyper-plane or set of hyper-planes. Intuitively, the hyper-plane, which has the greatest distance from the nearest training data points in any class, achieves a strong separation since, in general, the greater the margin, the lower the classifier’s generalization error. It is effective in high-dimensional spaces and can behave differently based on different mathematical functions known as the kernel. Linear, polynomial, radial basis function (RBF), sigmoid, etc., are the popular kernel functions used in SVM classifier [ 82 ]. However, when the data set contains more noise, such as overlapping target classes, SVM does not perform well.

An example of a decision tree structure

An example of a random forest structure considering multiple decision trees

• Adaptive Boosting (AdaBoost): Adaptive Boosting (AdaBoost) is an ensemble learning process that employs an iterative approach to improve poor classifiers by learning from their errors. This is developed by Yoav Freund et al. [ 35 ] and also known as “meta-learning”. Unlike the random forest that uses parallel ensembling, Adaboost uses “sequential ensembling”. It creates a powerful classifier by combining many poorly performing classifiers to obtain a good classifier of high accuracy. In that sense, AdaBoost is called an adaptive classifier by significantly improving the efficiency of the classifier, but in some instances, it can trigger overfits. AdaBoost is best used to boost the performance of decision trees, base estimator [ 82 ], on binary classification problems, however, is sensitive to noisy data and outliers.
• Extreme gradient boosting (XGBoost): Gradient Boosting, like Random Forests [ 19 ] above, is an ensemble learning algorithm that generates a final model based on a series of individual models, typically decision trees. The gradient is used to minimize the loss function, similar to how neural networks [ 41 ] use gradient descent to optimize weights. Extreme Gradient Boosting (XGBoost) is a form of gradient boosting that takes more detailed approximations into account when determining the best model [ 82 ]. It computes second-order gradients of the loss function to minimize loss and advanced regularization (L1 and L2) [ 82 ], which reduces over-fitting, and improves model generalization and performance. XGBoost is fast to interpret and can handle large-sized datasets well.
• Stochastic gradient descent (SGD): Stochastic gradient descent (SGD) [ 41 ] is an iterative method for optimizing an objective function with appropriate smoothness properties, where the word ‘stochastic’ refers to random probability. This reduces the computational burden, particularly in high-dimensional optimization problems, allowing for faster iterations in exchange for a lower convergence rate. A gradient is the slope of a function that calculates a variable’s degree of change in response to another variable’s changes. Mathematically, the Gradient Descent is a convex function whose output is a partial derivative of a set of its input parameters. Let, α is the learning rate, and J i is the training example cost of i th , then Eq. ( 4 ) represents the stochastic gradient descent weight update method at the j th iteration. In large-scale and sparse machine learning, SGD has been successfully applied to problems often encountered in text classification and natural language processing [ 82 ]. However, SGD is sensitive to feature scaling and needs a range of hyperparameters, such as the regularization parameter and the number of iterations. w j : = w j - α ∂ J i ∂ w j . 4
• Rule-based classification : The term rule-based classification can be used to refer to any classification scheme that makes use of IF-THEN rules for class prediction. Several classification algorithms such as Zero-R [ 125 ], One-R [ 47 ], decision trees [ 87 , 88 ], DTNB [ 110 ], Ripple Down Rule learner (RIDOR) [ 125 ], Repeated Incremental Pruning to Produce Error Reduction (RIPPER) [ 126 ] exist with the ability of rule generation. The decision tree is one of the most common rule-based classification algorithms among these techniques because it has several advantages, such as being easier to interpret; the ability to handle high-dimensional data; simplicity and speed; good accuracy; and the capability to produce rules for human clear and understandable classification [ 127 ] [ 128 ]. The decision tree-based rules also provide significant accuracy in a prediction model for unseen test cases [ 106 ]. Since the rules are easily interpretable, these rule-based classifiers are often used to produce descriptive models that can describe a system including the entities and their relationships.

Regression Analysis

Regression analysis includes several methods of machine learning that allow to predict a continuous ( y ) result variable based on the value of one or more ( x ) predictor variables [ 41 ]. The most significant distinction between classification and regression is that classification predicts distinct class labels, while regression facilitates the prediction of a continuous quantity. Figure ​ Figure6 6 shows an example of how classification is different with regression models. Some overlaps are often found between the two types of machine learning algorithms. Regression models are now widely used in a variety of fields, including financial forecasting or prediction, cost estimation, trend analysis, marketing, time series estimation, drug response modeling, and many more. Some of the familiar types of regression algorithms are linear, polynomial, lasso and ridge regression, etc., which are explained briefly in the following.

• Simple and multiple linear regression: This is one of the most popular ML modeling techniques as well as a well-known regression technique. In this technique, the dependent variable is continuous, the independent variable(s) can be continuous or discrete, and the form of the regression line is linear. Linear regression creates a relationship between the dependent variable ( Y ) and one or more independent variables ( X ) (also known as regression line) using the best fit straight line [ 41 ]. It is defined by the following equations: y = a + b x + e 5 y = a + b 1 x 1 + b 2 x 2 + ⋯ + b n x n + e , 6 where a is the intercept, b is the slope of the line, and e is the error term. This equation can be used to predict the value of the target variable based on the given predictor variable(s). Multiple linear regression is an extension of simple linear regression that allows two or more predictor variables to model a response variable, y, as a linear function [ 41 ] defined in Eq. 6 , whereas simple linear regression has only 1 independent variable, defined in Eq. 5 .
• Polynomial regression: Polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is not linear, but is the polynomial degree of n th in x [ 82 ]. The equation for polynomial regression is also derived from linear regression (polynomial regression of degree 1) equation, which is defined as below: y = b 0 + b 1 x + b 2 x 2 + b 3 x 3 + ⋯ + b n x n + e . 7 Here, y is the predicted/target output, b 0 , b 1 , . . . b n are the regression coefficients, x is an independent/ input variable. In simple words, we can say that if data are not distributed linearly, instead it is n th degree of polynomial then we use polynomial regression to get desired output.
• LASSO and ridge regression: LASSO and Ridge regression are well known as powerful techniques which are typically used for building learning models in presence of a large number of features, due to their capability to preventing over-fitting and reducing the complexity of the model. The LASSO (least absolute shrinkage and selection operator) regression model uses L 1 regularization technique [ 82 ] that uses shrinkage, which penalizes “absolute value of magnitude of coefficients” ( L 1 penalty). As a result, LASSO appears to render coefficients to absolute zero. Thus, LASSO regression aims to find the subset of predictors that minimizes the prediction error for a quantitative response variable. On the other hand, ridge regression uses L 2 regularization [ 82 ], which is the “squared magnitude of coefficients” ( L 2 penalty). Thus, ridge regression forces the weights to be small but never sets the coefficient value to zero, and does a non-sparse solution. Overall, LASSO regression is useful to obtain a subset of predictors by eliminating less important features, and ridge regression is useful when a data set has “multicollinearity” which refers to the predictors that are correlated with other predictors.

Classification vs. regression. In classification the dotted line represents a linear boundary that separates the two classes; in regression, the dotted line models the linear relationship between the two variables

Cluster Analysis

Cluster analysis, also known as clustering, is an unsupervised machine learning technique for identifying and grouping related data points in large datasets without concern for the specific outcome. It does grouping a collection of objects in such a way that objects in the same category, called a cluster, are in some sense more similar to each other than objects in other groups [ 41 ]. It is often used as a data analysis technique to discover interesting trends or patterns in data, e.g., groups of consumers based on their behavior. In a broad range of application areas, such as cybersecurity, e-commerce, mobile data processing, health analytics, user modeling and behavioral analytics, clustering can be used. In the following, we briefly discuss and summarize various types of clustering methods.

• Partitioning methods: Based on the features and similarities in the data, this clustering approach categorizes the data into multiple groups or clusters. The data scientists or analysts typically determine the number of clusters either dynamically or statically depending on the nature of the target applications, to produce for the methods of clustering. The most common clustering algorithms based on partitioning methods are K-means [ 69 ], K-Mediods [ 80 ], CLARA [ 55 ] etc.
• Density-based methods: To identify distinct groups or clusters, it uses the concept that a cluster in the data space is a contiguous region of high point density isolated from other such clusters by contiguous regions of low point density. Points that are not part of a cluster are considered as noise. The typical clustering algorithms based on density are DBSCAN [ 32 ], OPTICS [ 12 ] etc. The density-based methods typically struggle with clusters of similar density and high dimensionality data.

A graphical interpretation of the widely-used hierarchical clustering (Bottom-up and top-down) technique

• Grid-based methods: To deal with massive datasets, grid-based clustering is especially suitable. To obtain clusters, the principle is first to summarize the dataset with a grid representation and then to combine grid cells. STING [ 122 ], CLIQUE [ 6 ], etc. are the standard algorithms of grid-based clustering.
• Model-based methods: There are mainly two types of model-based clustering algorithms: one that uses statistical learning, and the other based on a method of neural network learning [ 130 ]. For instance, GMM [ 89 ] is an example of a statistical learning method, and SOM [ 22 ] [ 96 ] is an example of a neural network learning method.
• Constraint-based methods: Constrained-based clustering is a semi-supervised approach to data clustering that uses constraints to incorporate domain knowledge. Application or user-oriented constraints are incorporated to perform the clustering. The typical algorithms of this kind of clustering are COP K-means [ 121 ], CMWK-Means [ 27 ], etc.

Many clustering algorithms have been proposed with the ability to grouping data in machine learning and data science literature [ 41 , 125 ]. In the following, we summarize the popular methods that are used widely in various application areas.

• K-means clustering: K-means clustering [ 69 ] is a fast, robust, and simple algorithm that provides reliable results when data sets are well-separated from each other. The data points are allocated to a cluster in this algorithm in such a way that the amount of the squared distance between the data points and the centroid is as small as possible. In other words, the K-means algorithm identifies the k number of centroids and then assigns each data point to the nearest cluster while keeping the centroids as small as possible. Since it begins with a random selection of cluster centers, the results can be inconsistent. Since extreme values can easily affect a mean, the K-means clustering algorithm is sensitive to outliers. K-medoids clustering [ 91 ] is a variant of K-means that is more robust to noises and outliers.
• Mean-shift clustering: Mean-shift clustering [ 37 ] is a nonparametric clustering technique that does not require prior knowledge of the number of clusters or constraints on cluster shape. Mean-shift clustering aims to discover “blobs” in a smooth distribution or density of samples [ 82 ]. It is a centroid-based algorithm that works by updating centroid candidates to be the mean of the points in a given region. To form the final set of centroids, these candidates are filtered in a post-processing stage to remove near-duplicates. Cluster analysis in computer vision and image processing are examples of application domains. Mean Shift has the disadvantage of being computationally expensive. Moreover, in cases of high dimension, where the number of clusters shifts abruptly, the mean-shift algorithm does not work well.
• DBSCAN: Density-based spatial clustering of applications with noise (DBSCAN) [ 32 ] is a base algorithm for density-based clustering which is widely used in data mining and machine learning. This is known as a non-parametric density-based clustering technique for separating high-density clusters from low-density clusters that are used in model building. DBSCAN’s main idea is that a point belongs to a cluster if it is close to many points from that cluster. It can find clusters of various shapes and sizes in a vast volume of data that is noisy and contains outliers. DBSCAN, unlike k-means, does not require a priori specification of the number of clusters in the data and can find arbitrarily shaped clusters. Although k-means is much faster than DBSCAN, it is efficient at finding high-density regions and outliers, i.e., is robust to outliers.
• GMM clustering: Gaussian mixture models (GMMs) are often used for data clustering, which is a distribution-based clustering algorithm. A Gaussian mixture model is a probabilistic model in which all the data points are produced by a mixture of a finite number of Gaussian distributions with unknown parameters [ 82 ]. To find the Gaussian parameters for each cluster, an optimization algorithm called expectation-maximization (EM) [ 82 ] can be used. EM is an iterative method that uses a statistical model to estimate the parameters. In contrast to k-means, Gaussian mixture models account for uncertainty and return the likelihood that a data point belongs to one of the k clusters. GMM clustering is more robust than k-means and works well even with non-linear data distributions.
• Agglomerative hierarchical clustering: The most common method of hierarchical clustering used to group objects in clusters based on their similarity is agglomerative clustering. This technique uses a bottom-up approach, where each object is first treated as a singleton cluster by the algorithm. Following that, pairs of clusters are merged one by one until all clusters have been merged into a single large cluster containing all objects. The result is a dendrogram, which is a tree-based representation of the elements. Single linkage [ 115 ], Complete linkage [ 116 ], BOTS [ 102 ] etc. are some examples of such techniques. The main advantage of agglomerative hierarchical clustering over k-means is that the tree-structure hierarchy generated by agglomerative clustering is more informative than the unstructured collection of flat clusters returned by k-means, which can help to make better decisions in the relevant application areas.

Dimensionality Reduction and Feature Learning

In machine learning and data science, high-dimensional data processing is a challenging task for both researchers and application developers. Thus, dimensionality reduction which is an unsupervised learning technique, is important because it leads to better human interpretations, lower computational costs, and avoids overfitting and redundancy by simplifying models. Both the process of feature selection and feature extraction can be used for dimensionality reduction. The primary distinction between the selection and extraction of features is that the “feature selection” keeps a subset of the original features [ 97 ], while “feature extraction” creates brand new ones [ 98 ]. In the following, we briefly discuss these techniques.

• Feature selection: The selection of features, also known as the selection of variables or attributes in the data, is the process of choosing a subset of unique features (variables, predictors) to use in building machine learning and data science model. It decreases a model’s complexity by eliminating the irrelevant or less important features and allows for faster training of machine learning algorithms. A right and optimal subset of the selected features in a problem domain is capable to minimize the overfitting problem through simplifying and generalizing the model as well as increases the model’s accuracy [ 97 ]. Thus, “feature selection” [ 66 , 99 ] is considered as one of the primary concepts in machine learning that greatly affects the effectiveness and efficiency of the target machine learning model. Chi-squared test, Analysis of variance (ANOVA) test, Pearson’s correlation coefficient, recursive feature elimination, are some popular techniques that can be used for feature selection.
• Feature extraction: In a machine learning-based model or system, feature extraction techniques usually provide a better understanding of the data, a way to improve prediction accuracy, and to reduce computational cost or training time. The aim of “feature extraction” [ 66 , 99 ] is to reduce the number of features in a dataset by generating new ones from the existing ones and then discarding the original features. The majority of the information found in the original set of features can then be summarized using this new reduced set of features. For instance, principal components analysis (PCA) is often used as a dimensionality-reduction technique to extract a lower-dimensional space creating new brand components from the existing features in a dataset [ 98 ].

Many algorithms have been proposed to reduce data dimensions in the machine learning and data science literature [ 41 , 125 ]. In the following, we summarize the popular methods that are used widely in various application areas.

• Variance threshold: A simple basic approach to feature selection is the variance threshold [ 82 ]. This excludes all features of low variance, i.e., all features whose variance does not exceed the threshold. It eliminates all zero-variance characteristics by default, i.e., characteristics that have the same value in all samples. This feature selection algorithm looks only at the ( X ) features, not the ( y ) outputs needed, and can, therefore, be used for unsupervised learning.
• Pearson correlation: Pearson’s correlation is another method to understand a feature’s relation to the response variable and can be used for feature selection [ 99 ]. This method is also used for finding the association between the features in a dataset. The resulting value is [ - 1 , 1 ] , where - 1 means perfect negative correlation, + 1 means perfect positive correlation, and 0 means that the two variables do not have a linear correlation. If two random variables represent X and Y , then the correlation coefficient between X and Y is defined as [ 41 ] r ( X , Y ) = ∑ i = 1 n ( X i - X ¯ ) ( Y i - Y ¯ ) ∑ i = 1 n ( X i - X ¯ ) 2 ∑ i = 1 n ( Y i - Y ¯ ) 2 . 8
• ANOVA: Analysis of variance (ANOVA) is a statistical tool used to verify the mean values of two or more groups that differ significantly from each other. ANOVA assumes a linear relationship between the variables and the target and the variables’ normal distribution. To statistically test the equality of means, the ANOVA method utilizes F tests. For feature selection, the results ‘ANOVA F value’ [ 82 ] of this test can be used where certain features independent of the goal variable can be omitted.
• Chi square: The chi-square χ 2 [ 82 ] statistic is an estimate of the difference between the effects of a series of events or variables observed and expected frequencies. The magnitude of the difference between the real and observed values, the degrees of freedom, and the sample size depends on χ 2 . The chi-square χ 2 is commonly used for testing relationships between categorical variables. If O i represents observed value and E i represents expected value, then χ 2 = ∑ i = 1 n ( O i - E i ) 2 E i . 9
• Recursive feature elimination (RFE): Recursive Feature Elimination (RFE) is a brute force approach to feature selection. RFE [ 82 ] fits the model and removes the weakest feature before it meets the specified number of features. Features are ranked by the coefficients or feature significance of the model. RFE aims to remove dependencies and collinearity in the model by recursively removing a small number of features per iteration.
• Model-based selection: To reduce the dimensionality of the data, linear models penalized with the L 1 regularization can be used. Least absolute shrinkage and selection operator (Lasso) regression is a type of linear regression that has the property of shrinking some of the coefficients to zero [ 82 ]. Therefore, that feature can be removed from the model. Thus, the penalized lasso regression method, often used in machine learning to select the subset of variables. Extra Trees Classifier [ 82 ] is an example of a tree-based estimator that can be used to compute impurity-based function importance, which can then be used to discard irrelevant features.

An example of a principal component analysis (PCA) and created principal components PC1 and PC2 in different dimension space

Association Rule Learning

Association rule learning is a rule-based machine learning approach to discover interesting relationships, “IF-THEN” statements, in large datasets between variables [ 7 ]. One example is that “if a customer buys a computer or laptop (an item), s/he is likely to also buy anti-virus software (another item) at the same time”. Association rules are employed today in many application areas, including IoT services, medical diagnosis, usage behavior analytics, web usage mining, smartphone applications, cybersecurity applications, and bioinformatics. In comparison to sequence mining, association rule learning does not usually take into account the order of things within or across transactions. A common way of measuring the usefulness of association rules is to use its parameter, the ‘support’ and ‘confidence’, which is introduced in [ 7 ].

In the data mining literature, many association rule learning methods have been proposed, such as logic dependent [ 34 ], frequent pattern based [ 8 , 49 , 68 ], and tree-based [ 42 ]. The most popular association rule learning algorithms are summarized below.

• AIS and SETM: AIS is the first algorithm proposed by Agrawal et al. [ 7 ] for association rule mining. The AIS algorithm’s main downside is that too many candidate itemsets are generated, requiring more space and wasting a lot of effort. This algorithm calls for too many passes over the entire dataset to produce the rules. Another approach SETM [ 49 ] exhibits good performance and stable behavior with execution time; however, it suffers from the same flaw as the AIS algorithm.
• Apriori: For generating association rules for a given dataset, Agrawal et al. [ 8 ] proposed the Apriori, Apriori-TID, and Apriori-Hybrid algorithms. These later algorithms outperform the AIS and SETM mentioned above due to the Apriori property of frequent itemset [ 8 ]. The term ‘Apriori’ usually refers to having prior knowledge of frequent itemset properties. Apriori uses a “bottom-up” approach, where it generates the candidate itemsets. To reduce the search space, Apriori uses the property “all subsets of a frequent itemset must be frequent; and if an itemset is infrequent, then all its supersets must also be infrequent”. Another approach predictive Apriori [ 108 ] can also generate rules; however, it receives unexpected results as it combines both the support and confidence. The Apriori [ 8 ] is the widely applicable techniques in mining association rules.
• ECLAT: This technique was proposed by Zaki et al. [ 131 ] and stands for Equivalence Class Clustering and bottom-up Lattice Traversal. ECLAT uses a depth-first search to find frequent itemsets. In contrast to the Apriori [ 8 ] algorithm, which represents data in a horizontal pattern, it represents data vertically. Hence, the ECLAT algorithm is more efficient and scalable in the area of association rule learning. This algorithm is better suited for small and medium datasets whereas the Apriori algorithm is used for large datasets.
• FP-Growth: Another common association rule learning technique based on the frequent-pattern tree (FP-tree) proposed by Han et al. [ 42 ] is Frequent Pattern Growth, known as FP-Growth. The key difference with Apriori is that while generating rules, the Apriori algorithm [ 8 ] generates frequent candidate itemsets; on the other hand, the FP-growth algorithm [ 42 ] prevents candidate generation and thus produces a tree by the successful strategy of ‘divide and conquer’ approach. Due to its sophistication, however, FP-Tree is challenging to use in an interactive mining environment [ 133 ]. Thus, the FP-Tree would not fit into memory for massive data sets, making it challenging to process big data as well. Another solution is RARM (Rapid Association Rule Mining) proposed by Das et al. [ 26 ] but faces a related FP-tree issue [ 133 ].
• ABC-RuleMiner: A rule-based machine learning method, recently proposed in our earlier paper, by Sarker et al. [ 104 ], to discover the interesting non-redundant rules to provide real-world intelligent services. This algorithm effectively identifies the redundancy in associations by taking into account the impact or precedence of the related contextual features and discovers a set of non-redundant association rules. This algorithm first constructs an association generation tree (AGT), a top-down approach, and then extracts the association rules through traversing the tree. Thus, ABC-RuleMiner is more potent than traditional rule-based methods in terms of both non-redundant rule generation and intelligent decision-making, particularly in a context-aware smart computing environment, where human or user preferences are involved.

Among the association rule learning techniques discussed above, Apriori [ 8 ] is the most widely used algorithm for discovering association rules from a given dataset [ 133 ]. The main strength of the association learning technique is its comprehensiveness, as it generates all associations that satisfy the user-specified constraints, such as minimum support and confidence value. The ABC-RuleMiner approach [ 104 ] discussed earlier could give significant results in terms of non-redundant rule generation and intelligent decision-making for the relevant application areas in the real world.

Reinforcement Learning

Reinforcement learning (RL) is a machine learning technique that allows an agent to learn by trial and error in an interactive environment using input from its actions and experiences. Unlike supervised learning, which is based on given sample data or examples, the RL method is based on interacting with the environment. The problem to be solved in reinforcement learning (RL) is defined as a Markov Decision Process (MDP) [ 86 ], i.e., all about sequentially making decisions. An RL problem typically includes four elements such as Agent, Environment, Rewards, and Policy.

RL can be split roughly into Model-based and Model-free techniques. Model-based RL is the process of inferring optimal behavior from a model of the environment by performing actions and observing the results, which include the next state and the immediate reward [ 85 ]. AlphaZero, AlphaGo [ 113 ] are examples of the model-based approaches. On the other hand, a model-free approach does not use the distribution of the transition probability and the reward function associated with MDP. Q-learning, Deep Q Network, Monte Carlo Control, SARSA (State–Action–Reward–State–Action), etc. are some examples of model-free algorithms [ 52 ]. The policy network, which is required for model-based RL but not for model-free, is the key difference between model-free and model-based learning. In the following, we discuss the popular RL algorithms.

• Monte Carlo methods: Monte Carlo techniques, or Monte Carlo experiments, are a wide category of computational algorithms that rely on repeated random sampling to obtain numerical results [ 52 ]. The underlying concept is to use randomness to solve problems that are deterministic in principle. Optimization, numerical integration, and making drawings from the probability distribution are the three problem classes where Monte Carlo techniques are most commonly used.
• Q-learning: Q-learning is a model-free reinforcement learning algorithm for learning the quality of behaviors that tell an agent what action to take under what conditions [ 52 ]. It does not need a model of the environment (hence the term “model-free”), and it can deal with stochastic transitions and rewards without the need for adaptations. The ‘Q’ in Q-learning usually stands for quality, as the algorithm calculates the maximum expected rewards for a given behavior in a given state.
• Deep Q-learning: The basic working step in Deep Q-Learning [ 52 ] is that the initial state is fed into the neural network, which returns the Q-value of all possible actions as an output. Still, when we have a reasonably simple setting to overcome, Q-learning works well. However, when the number of states and actions becomes more complicated, deep learning can be used as a function approximator.

Reinforcement learning, along with supervised and unsupervised learning, is one of the basic machine learning paradigms. RL can be used to solve numerous real-world problems in various fields, such as game theory, control theory, operations analysis, information theory, simulation-based optimization, manufacturing, supply chain logistics, multi-agent systems, swarm intelligence, aircraft control, robot motion control, and many more.

Artificial Neural Network and Deep Learning

Deep learning is part of a wider family of artificial neural networks (ANN)-based machine learning approaches with representation learning. Deep learning provides a computational architecture by combining several processing layers, such as input, hidden, and output layers, to learn from data [ 41 ]. The main advantage of deep learning over traditional machine learning methods is its better performance in several cases, particularly learning from large datasets [ 105 , 129 ]. Figure ​ Figure9 9 shows a general performance of deep learning over machine learning considering the increasing amount of data. However, it may vary depending on the data characteristics and experimental set up.

Machine learning and deep learning performance in general with the amount of data

The most common deep learning algorithms are: Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN, or ConvNet), Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) [ 96 ]. In the following, we discuss various types of deep learning methods that can be used to build effective data-driven models for various purposes.

A structure of an artificial neural network modeling with multiple processing layers

An example of a convolutional neural network (CNN or ConvNet) including multiple convolution and pooling layers

• LSTM-RNN: Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the area of deep learning [ 38 ]. LSTM has feedback links, unlike normal feed-forward neural networks. LSTM networks are well-suited for analyzing and learning sequential data, such as classifying, processing, and predicting data based on time series data, which differentiates it from other conventional networks. Thus, LSTM can be used when the data are in a sequential format, such as time, sentence, etc., and commonly applied in the area of time-series analysis, natural language processing, speech recognition, etc.

In addition to these most common deep learning methods discussed above, several other deep learning approaches [ 96 ] exist in the area for various purposes. For instance, the self-organizing map (SOM) [ 58 ] uses unsupervised learning to represent the high-dimensional data by a 2D grid map, thus achieving dimensionality reduction. The autoencoder (AE) [ 15 ] is another learning technique that is widely used for dimensionality reduction as well and feature extraction in unsupervised learning tasks. Restricted Boltzmann machines (RBM) [ 46 ] can be used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling. A deep belief network (DBN) is typically composed of simple, unsupervised networks such as restricted Boltzmann machines (RBMs) or autoencoders, and a backpropagation neural network (BPNN) [ 123 ]. A generative adversarial network (GAN) [ 39 ] is a form of the network for deep learning that can generate data with characteristics close to the actual data input. Transfer learning is currently very common because it can train deep neural networks with comparatively low data, which is typically the re-use of a new problem with a pre-trained model [ 124 ]. A brief discussion of these artificial neural networks (ANN) and deep learning (DL) models are summarized in our earlier paper Sarker et al. [ 96 ].

Overall, based on the learning techniques discussed above, we can conclude that various types of machine learning techniques, such as classification analysis, regression, data clustering, feature selection and extraction, and dimensionality reduction, association rule learning, reinforcement learning, or deep learning techniques, can play a significant role for various purposes according to their capabilities. In the following section, we discuss several application areas based on machine learning algorithms.

Applications of Machine Learning

In the current age of the Fourth Industrial Revolution (4IR), machine learning becomes popular in various application areas, because of its learning capabilities from the past and making intelligent decisions. In the following, we summarize and discuss ten popular application areas of machine learning technology.

• Predictive analytics and intelligent decision-making: A major application field of machine learning is intelligent decision-making by data-driven predictive analytics [ 21 , 70 ]. The basis of predictive analytics is capturing and exploiting relationships between explanatory variables and predicted variables from previous events to predict the unknown outcome [ 41 ]. For instance, identifying suspects or criminals after a crime has been committed, or detecting credit card fraud as it happens. Another application, where machine learning algorithms can assist retailers in better understanding consumer preferences and behavior, better manage inventory, avoiding out-of-stock situations, and optimizing logistics and warehousing in e-commerce. Various machine learning algorithms such as decision trees, support vector machines, artificial neural networks, etc. [ 106 , 125 ] are commonly used in the area. Since accurate predictions provide insight into the unknown, they can improve the decisions of industries, businesses, and almost any organization, including government agencies, e-commerce, telecommunications, banking and financial services, healthcare, sales and marketing, transportation, social networking, and many others.
• Cybersecurity and threat intelligence: Cybersecurity is one of the most essential areas of Industry 4.0. [ 114 ], which is typically the practice of protecting networks, systems, hardware, and data from digital attacks [ 114 ]. Machine learning has become a crucial cybersecurity technology that constantly learns by analyzing data to identify patterns, better detect malware in encrypted traffic, find insider threats, predict where bad neighborhoods are online, keep people safe while browsing, or secure data in the cloud by uncovering suspicious activity. For instance, clustering techniques can be used to identify cyber-anomalies, policy violations, etc. To detect various types of cyber-attacks or intrusions machine learning classification models by taking into account the impact of security features are useful [ 97 ]. Various deep learning-based security models can also be used on the large scale of security datasets [ 96 , 129 ]. Moreover, security policy rules generated by association rule learning techniques can play a significant role to build a rule-based security system [ 105 ]. Thus, we can say that various learning techniques discussed in Sect. Machine Learning Tasks and Algorithms , can enable cybersecurity professionals to be more proactive inefficiently preventing threats and cyber-attacks.
• Internet of things (IoT) and smart cities: Internet of Things (IoT) is another essential area of Industry 4.0. [ 114 ], which turns everyday objects into smart objects by allowing them to transmit data and automate tasks without the need for human interaction. IoT is, therefore, considered to be the big frontier that can enhance almost all activities in our lives, such as smart governance, smart home, education, communication, transportation, retail, agriculture, health care, business, and many more [ 70 ]. Smart city is one of IoT’s core fields of application, using technologies to enhance city services and residents’ living experiences [ 132 , 135 ]. As machine learning utilizes experience to recognize trends and create models that help predict future behavior and events, it has become a crucial technology for IoT applications [ 103 ]. For example, to predict traffic in smart cities, parking availability prediction, estimate the total usage of energy of the citizens for a particular period, make context-aware and timely decisions for the people, etc. are some tasks that can be solved using machine learning techniques according to the current needs of the people.
• Traffic prediction and transportation: Transportation systems have become a crucial component of every country’s economic development. Nonetheless, several cities around the world are experiencing an excessive rise in traffic volume, resulting in serious issues such as delays, traffic congestion, higher fuel prices, increased CO 2 pollution, accidents, emergencies, and a decline in modern society’s quality of life [ 40 ]. Thus, an intelligent transportation system through predicting future traffic is important, which is an indispensable part of a smart city. Accurate traffic prediction based on machine and deep learning modeling can help to minimize the issues [ 17 , 30 , 31 ]. For example, based on the travel history and trend of traveling through various routes, machine learning can assist transportation companies in predicting possible issues that may occur on specific routes and recommending their customers to take a different path. Ultimately, these learning-based data-driven models help improve traffic flow, increase the usage and efficiency of sustainable modes of transportation, and limit real-world disruption by modeling and visualizing future changes.
• Healthcare and COVID-19 pandemic: Machine learning can help to solve diagnostic and prognostic problems in a variety of medical domains, such as disease prediction, medical knowledge extraction, detecting regularities in data, patient management, etc. [ 33 , 77 , 112 ]. Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus, according to the World Health Organization (WHO) [ 3 ]. Recently, the learning techniques have become popular in the battle against COVID-19 [ 61 , 63 ]. For the COVID-19 pandemic, the learning techniques are used to classify patients at high risk, their mortality rate, and other anomalies [ 61 ]. It can also be used to better understand the virus’s origin, COVID-19 outbreak prediction, as well as for disease diagnosis and treatment [ 14 , 50 ]. With the help of machine learning, researchers can forecast where and when, the COVID-19 is likely to spread, and notify those regions to match the required arrangements. Deep learning also provides exciting solutions to the problems of medical image processing and is seen as a crucial technique for potential applications, particularly for COVID-19 pandemic [ 10 , 78 , 111 ]. Overall, machine and deep learning techniques can help to fight the COVID-19 virus and the pandemic as well as intelligent clinical decisions making in the domain of healthcare.
• E-commerce and product recommendations: Product recommendation is one of the most well known and widely used applications of machine learning, and it is one of the most prominent features of almost any e-commerce website today. Machine learning technology can assist businesses in analyzing their consumers’ purchasing histories and making customized product suggestions for their next purchase based on their behavior and preferences. E-commerce companies, for example, can easily position product suggestions and offers by analyzing browsing trends and click-through rates of specific items. Using predictive modeling based on machine learning techniques, many online retailers, such as Amazon [ 71 ], can better manage inventory, prevent out-of-stock situations, and optimize logistics and warehousing. The future of sales and marketing is the ability to capture, evaluate, and use consumer data to provide a customized shopping experience. Furthermore, machine learning techniques enable companies to create packages and content that are tailored to the needs of their customers, allowing them to maintain existing customers while attracting new ones.
• NLP and sentiment analysis: Natural language processing (NLP) involves the reading and understanding of spoken or written language through the medium of a computer [ 79 , 103 ]. Thus, NLP helps computers, for instance, to read a text, hear speech, interpret it, analyze sentiment, and decide which aspects are significant, where machine learning techniques can be used. Virtual personal assistant, chatbot, speech recognition, document description, language or machine translation, etc. are some examples of NLP-related tasks. Sentiment Analysis [ 90 ] (also referred to as opinion mining or emotion AI) is an NLP sub-field that seeks to identify and extract public mood and views within a given text through blogs, reviews, social media, forums, news, etc. For instance, businesses and brands use sentiment analysis to understand the social sentiment of their brand, product, or service through social media platforms or the web as a whole. Overall, sentiment analysis is considered as a machine learning task that analyzes texts for polarity, such as “positive”, “negative”, or “neutral” along with more intense emotions like very happy, happy, sad, very sad, angry, have interest, or not interested etc.
• Image, speech and pattern recognition: Image recognition [ 36 ] is a well-known and widespread example of machine learning in the real world, which can identify an object as a digital image. For instance, to label an x-ray as cancerous or not, character recognition, or face detection in an image, tagging suggestions on social media, e.g., Facebook, are common examples of image recognition. Speech recognition [ 23 ] is also very popular that typically uses sound and linguistic models, e.g., Google Assistant, Cortana, Siri, Alexa, etc. [ 67 ], where machine learning methods are used. Pattern recognition [ 13 ] is defined as the automated recognition of patterns and regularities in data, e.g., image analysis. Several machine learning techniques such as classification, feature selection, clustering, or sequence labeling methods are used in the area.
• Sustainable agriculture: Agriculture is essential to the survival of all human activities [ 109 ]. Sustainable agriculture practices help to improve agricultural productivity while also reducing negative impacts on the environment [ 5 , 25 , 109 ]. The sustainable agriculture supply chains are knowledge-intensive and based on information, skills, technologies, etc., where knowledge transfer encourages farmers to enhance their decisions to adopt sustainable agriculture practices utilizing the increasing amount of data captured by emerging technologies, e.g., the Internet of Things (IoT), mobile technologies and devices, etc. [ 5 , 53 , 54 ]. Machine learning can be applied in various phases of sustainable agriculture, such as in the pre-production phase - for the prediction of crop yield, soil properties, irrigation requirements, etc.; in the production phase—for weather prediction, disease detection, weed detection, soil nutrient management, livestock management, etc.; in processing phase—for demand estimation, production planning, etc. and in the distribution phase - the inventory management, consumer analysis, etc.
• User behavior analytics and context-aware smartphone applications: Context-awareness is a system’s ability to capture knowledge about its surroundings at any moment and modify behaviors accordingly [ 28 , 93 ]. Context-aware computing uses software and hardware to automatically collect and interpret data for direct responses. The mobile app development environment has been changed greatly with the power of AI, particularly, machine learning techniques through their learning capabilities from contextual data [ 103 , 136 ]. Thus, the developers of mobile apps can rely on machine learning to create smart apps that can understand human behavior, support, and entertain users [ 107 , 137 , 140 ]. To build various personalized data-driven context-aware systems, such as smart interruption management, smart mobile recommendation, context-aware smart searching, decision-making that intelligently assist end mobile phone users in a pervasive computing environment, machine learning techniques are applicable. For example, context-aware association rules can be used to build an intelligent phone call application [ 104 ]. Clustering approaches are useful in capturing users’ diverse behavioral activities by taking into account data in time series [ 102 ]. To predict the future events in various contexts, the classification methods can be used [ 106 , 139 ]. Thus, various learning techniques discussed in Sect. “ Machine Learning Tasks and Algorithms ” can help to build context-aware adaptive and smart applications according to the preferences of the mobile phone users.

In addition to these application areas, machine learning-based models can also apply to several other domains such as bioinformatics, cheminformatics, computer networks, DNA sequence classification, economics and banking, robotics, advanced engineering, and many more.

Challenges and Research Directions

Our study on machine learning algorithms for intelligent data analysis and applications opens several research issues in the area. Thus, in this section, we summarize and discuss the challenges faced and the potential research opportunities and future directions.

In general, the effectiveness and the efficiency of a machine learning-based solution depend on the nature and characteristics of the data, and the performance of the learning algorithms. To collect the data in the relevant domain, such as cybersecurity, IoT, healthcare and agriculture discussed in Sect. “ Applications of Machine Learning ” is not straightforward, although the current cyberspace enables the production of a huge amount of data with very high frequency. Thus, collecting useful data for the target machine learning-based applications, e.g., smart city applications, and their management is important to further analysis. Therefore, a more in-depth investigation of data collection methods is needed while working on the real-world data. Moreover, the historical data may contain many ambiguous values, missing values, outliers, and meaningless data. The machine learning algorithms, discussed in Sect “ Machine Learning Tasks and Algorithms ” highly impact on data quality, and availability for training, and consequently on the resultant model. Thus, to accurately clean and pre-process the diverse data collected from diverse sources is a challenging task. Therefore, effectively modifying or enhance existing pre-processing methods, or proposing new data preparation techniques are required to effectively use the learning algorithms in the associated application domain.

To analyze the data and extract insights, there exist many machine learning algorithms, summarized in Sect. “ Machine Learning Tasks and Algorithms ”. Thus, selecting a proper learning algorithm that is suitable for the target application is challenging. The reason is that the outcome of different learning algorithms may vary depending on the data characteristics [ 106 ]. Selecting a wrong learning algorithm would result in producing unexpected outcomes that may lead to loss of effort, as well as the model’s effectiveness and accuracy. In terms of model building, the techniques discussed in Sect. “ Machine Learning Tasks and Algorithms ” can directly be used to solve many real-world issues in diverse domains, such as cybersecurity, smart cities and healthcare summarized in Sect. “ Applications of Machine Learning ”. However, the hybrid learning model, e.g., the ensemble of methods, modifying or enhancement of the existing learning techniques, or designing new learning methods, could be a potential future work in the area.

Thus, the ultimate success of a machine learning-based solution and corresponding applications mainly depends on both the data and the learning algorithms. If the data are bad to learn, such as non-representative, poor-quality, irrelevant features, or insufficient quantity for training, then the machine learning models may become useless or will produce lower accuracy. Therefore, effectively processing the data and handling the diverse learning algorithms are important, for a machine learning-based solution and eventually building intelligent applications.

In this paper, we have conducted a comprehensive overview of machine learning algorithms for intelligent data analysis and applications. According to our goal, we have briefly discussed how various types of machine learning methods can be used for making solutions to various real-world issues. A successful machine learning model depends on both the data and the performance of the learning algorithms. The sophisticated learning algorithms then need to be trained through the collected real-world data and knowledge related to the target application before the system can assist with intelligent decision-making. We also discussed several popular application areas based on machine learning techniques to highlight their applicability in various real-world issues. Finally, we have summarized and discussed the challenges faced and the potential research opportunities and future directions in the area. Therefore, the challenges that are identified create promising research opportunities in the field which must be addressed with effective solutions in various application areas. Overall, we believe that our study on machine learning-based solutions opens up a promising direction and can be used as a reference guide for potential research and applications for both academia and industry professionals as well as for decision-makers, from a technical point of view.

Declaration

The author declares no conflict of interest.

This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar.

Publisher's Note

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PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

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