machine learning master thesis

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Top 10 Research and Thesis Topics for ML Projects in 2022

This article features the top 10 research and thesis topics for ML projects for students to try in 2022

Text mining and text classification, image-based applications, machine vision, optimization, voice classification, sentiment analysis, recommendation framework project, mall customers’ project, object detection with deep learning.

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Open Topics

We offer multiple Bachelor/Master theses, Guided Research projects and IDPs in the area of data mining/machine learning. A  non-exhaustive list of open topics is listed below.

If you are interested in a thesis or a guided research project, please send your CV and transcript of records to Prof. Stephan Günnemann via email and we will arrange a meeting to talk about the potential topics.

Robustness of Large Language Models

Type: Master's Thesis

Prerequisites:

• Strong knowledge in machine learning
• Very good coding skills
• Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch)
• Knowledge about NLP and LLMs

Description:

The success of Large Language Models (LLMs) has precipitated their deployment across a diverse range of applications. With the integration of plugins enhancing their capabilities, it becomes imperative to ensure that the governing rules of these LLMs are foolproof and immune to circumvention. Recent studies have exposed significant vulnerabilities inherent to these models, underlining an urgent need for more rigorous research to fortify their resilience and reliability. A focus in this work will be the understanding of the working mechanisms of these attacks.

We are currently seeking students for the upcoming Summer Semester of 2024, so we welcome prompt applications. This project is in collaboration with  Google Research .

Contact: Tom Wollschläger

References:

• Universal and Transferable Adversarial Attacks on Aligned Language Models
• Attacking Large Language Models with Projected Gradient Descent
• Representation Engineering: A Top-Down Approach to AI Transparency
• Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks

Generative Models for Drug Discovery

Type:  Mater Thesis / Guided Research

• Strong machine learning knowledge
• Proficiency with Python and deep learning frameworks (PyTorch or TensorFlow)
• Knowledge of graph neural networks (e.g. GCN, MPNN)
• No formal education in chemistry, physics or biology needed!

Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain which has experienced great attention through the success of generative models. These models promise a more efficient exploration of the vast chemical space and generation of novel compounds with specific properties by leveraging their learned representations, potentially leading to the discovery of molecules with unique properties that would otherwise go undiscovered. Our topics lie at the intersection of generative models like diffusion/flow matching models and graph representation learning, e.g., graph neural networks. The focus of our projects can be model development with an emphasis on downstream tasks ( e.g., diffusion guidance at inference time ) and a better understanding of the limitations of existing models.

Contact :  Johanna Sommer , Leon Hetzel

Equivariant Diffusion for Molecule Generation in 3D

Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation

Structure-based Drug Design with Equivariant Diffusion Models

Data Pruning and Active Learning

Type: Interdisciplinary Project (IDP) / Hiwi / Guided Research / Master's Thesis

Data pruning and active learning are vital techniques in scaling machine learning applications efficiently. Data pruning involves the removal of redundant or irrelevant data, which enables training models with considerably less data but the same performance. Similarly, active learning describes the process of selecting the most informative data points for labeling, thus reducing annotation costs and accelerating model training. However, current methods are often computationally expensive, which makes them difficult to apply in practice. Our objective is to scale active learning and data pruning methods to large datasets using an extrapolation-based approach.

Contact: Sebastian Schmidt , Tom Wollschläger , Leo Schwinn

• Large-scale Dataset Pruning with Dynamic Uncertainty

Efficient Machine Learning: Pruning, Quantization, Distillation, and More - DAML x Pruna AI

Type: Master's Thesis / Guided Research / Hiwi

The efficiency of machine learning algorithms is commonly evaluated by looking at target performance, speed and memory footprint metrics. Reduce the costs associated to these metrics is of primary importance for real-world applications with limited ressources (e.g. embedded systems, real-time predictions). In this project, you will work in collaboration with the DAML research group and the Pruna AI startup on investigating solutions to improve the efficiency of machine leanring models by looking at multiple techniques like pruning, quantization, distillation, and more.

Contact: Bertrand Charpentier

• The Efficiency Misnomer
• A Gradient Flow Framework for Analyzing Network Pruning
• Distilling the Knowledge in a Neural Network
• A Survey of Quantization Methods for Efficient Neural Network Inference

Deep Generative Models

Type:  Master Thesis / Guided Research

• Strong machine learning and probability theory knowledge
• Knowledge of generative models and their basics (e.g., Normalizing Flows, Diffusion Models, VAE)
• Optional: Neural ODEs/SDEs, Optimal Transport, Measure Theory

With recent advances, such as Diffusion Models, Transformers, Normalizing Flows, Flow Matching, etc., the field of generative models has gained significant attention in the machine learning and artificial intelligence research community. However, many problems and questions remain open, and the application to complex data domains such as graphs, time series, point processes, and sets is often non-trivial. We are interested in supervising motivated students to explore and extend the capabilities of state-of-the-art generative models for various data domains.

Contact : Marcel Kollovieh , David Lüdke

• Flow Matching for Generative Modeling
• Auto-Encoding Variational Bayes
• Denoising Diffusion Probabilistic Models 
• Structured Denoising Diffusion Models in Discrete State-Spaces

Graph Structure Learning

Type:  Guided Research / Hiwi

• Optional: Knowledge of graph theory and mathematical optimization

Graph deep learning is a powerful ML concept that enables the generalisation of successful deep neural architectures to non-Euclidean structured data. Such methods have shown promising results in a vast range of applications spanning the social sciences, biomedicine, particle physics, computer vision, graphics and chemistry. One of the major limitations of most current graph neural network architectures is that they often rely on the assumption that the underlying graph is known and fixed. However, this assumption is not always true, as the graph may be noisy or partially and even completely unknown. In the case of noisy or partially available graphs, it would be useful to jointly learn an optimised graph structure and the corresponding graph representations for the downstream task. On the other hand, when the graph is completely absent, it would be useful to infer it directly from the data. This is particularly interesting in inductive settings where some of the nodes were not present at training time. Furthermore, learning a graph can become an end in itself, as the inferred structure can provide complementary insights with respect to the downstream task. In this project, we aim to investigate solutions and devise new methods to construct an optimal graph structure based on the available (unstructured) data.

Contact : Filippo Guerranti

• A Survey on Graph Structure Learning: Progress and Opportunities
• Differentiable Graph Module (DGM) for Graph Convolutional Networks
• Learning Discrete Structures for Graph Neural Networks

NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification

A Machine Learning Perspective on Corner Cases in Autonomous Driving Perception  

Type: Master's Thesis 

Industrial partner: BMW 

Prerequisites: 

• Strong knowledge in machine learning 
• Knowledge of Semantic Segmentation  
• Good programming skills 
• Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch) 

Description: 

In autonomous driving, state-of-the-art deep neural networks are used for perception tasks like for example semantic segmentation. While the environment in datasets is controlled in real world application novel class or unknown disturbances can occur. To provide safe autonomous driving these cased must be identified. 

The objective is to explore novel class segmentation and out of distribution approaches for semantic segmentation in the context of corner cases for autonomous driving. 

Contact: Sebastian Schmidt

References: 

• Segmenting Known Objects and Unseen Unknowns without Prior Knowledge 
• Efficient Uncertainty Estimation for Semantic Segmentation in Videos  
• Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family  
• Description of Corner Cases in Automated Driving: Goals and Challenges 

Active Learning for Multi Agent 3D Object Detection 

Type: Master's Thesis  Industrial partner: BMW 

• Knowledge in Object Detection 
• Excellent programming skills 

In autonomous driving, state-of-the-art deep neural networks are used for perception tasks like for example 3D object detection. To provide promising results, these networks often require a lot of complex annotation data for training. These annotations are often costly and redundant. Active learning is used to select the most informative samples for annotation and cover a dataset with as less annotated data as possible.   

The objective is to explore active learning approaches for 3D object detection using combined uncertainty and diversity based methods.  

• Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving   
• Efficient Uncertainty Estimation for Semantic Segmentation in Videos   
• KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
• Towards Open World Active Learning for 3D Object Detection   

Graph Neural Networks

Type:  Master's thesis / Bachelor's thesis / guided research

• Knowledge of graph/network theory

Graph neural networks (GNNs) have recently achieved great successes in a wide variety of applications, such as chemistry, reinforcement learning, knowledge graphs, traffic networks, or computer vision. These models leverage graph data by updating node representations based on messages passed between nodes connected by edges, or by transforming node representation using spectral graph properties. These approaches are very effective, but many theoretical aspects of these models remain unclear and there are many possible extensions to improve GNNs and go beyond the nodes' direct neighbors and simple message aggregation.

Contact: Simon Geisler

• Semi-supervised classification with graph convolutional networks
• Relational inductive biases, deep learning, and graph networks
• Diffusion Improves Graph Learning
• Weisfeiler and leman go neural: Higher-order graph neural networks
• Reliable Graph Neural Networks via Robust Aggregation

Physics-aware Graph Neural Networks

Type:  Master's thesis / guided research

• Proficiency with Python and deep learning frameworks (JAX or PyTorch)
• Knowledge of graph neural networks (e.g. GCN, MPNN, SchNet)
• Optional: Knowledge of machine learning on molecules and quantum chemistry

Deep learning models, especially graph neural networks (GNNs), have recently achieved great successes in predicting quantum mechanical properties of molecules. There is a vast amount of applications for these models, such as finding the best method of chemical synthesis or selecting candidates for drugs, construction materials, batteries, or solar cells. However, GNNs have only been proposed in recent years and there remain many open questions about how to best represent and leverage quantum mechanical properties and methods.

Contact: Nicholas Gao

• Directional Message Passing for Molecular Graphs
• Neural message passing for quantum chemistry
• Learning to Simulate Complex Physics with Graph Network
• Ab initio solution of the many-electron Schrödinger equation with deep neural networks
• Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
• Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Robustness Verification for Deep Classifiers

Type: Master's thesis / Guided research

• Strong machine learning knowledge (at least equivalent to IN2064 plus an advanced course on deep learning)
• Strong background in mathematical optimization (preferably combined with Machine Learning setting)
• Proficiency with python and deep learning frameworks (Pytorch or Tensorflow)
• (Preferred) Knowledge of training techniques to obtain classifiers that are robust against small perturbations in data

Description : Recent work shows that deep classifiers suffer under presence of adversarial examples: misclassified points that are very close to the training samples or even visually indistinguishable from them. This undesired behaviour constraints possibilities of deployment in safety critical scenarios for promising classification methods based on neural nets. Therefore, new training methods should be proposed that promote (or preferably ensure) robust behaviour of the classifier around training samples.

Contact: Aleksei Kuvshinov

References (Background):

• Intriguing properties of neural networks
• Explaining and harnessing adversarial examples
• SoK: Certified Robustness for Deep Neural Networks
• Certified Adversarial Robustness via Randomized Smoothing
• Formal guarantees on the robustness of a classifier against adversarial manipulation
• Towards deep learning models resistant to adversarial attacks
• Provable defenses against adversarial examples via the convex outer adversarial polytope
• Certified defenses against adversarial examples
• Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks

Uncertainty Estimation in Deep Learning

Type: Master's Thesis / Guided Research

• Strong knowledge in probability theory

Safe prediction is a key feature in many intelligent systems. Classically, Machine Learning models compute output predictions regardless of the underlying uncertainty of the encountered situations. In contrast, aleatoric and epistemic uncertainty bring knowledge about undecidable and uncommon situations. The uncertainty view can be a substantial help to detect and explain unsafe predictions, and therefore make ML systems more robust. The goal of this project is to improve the uncertainty estimation in ML models in various types of task.

Contact: Tom Wollschläger ,   Dominik Fuchsgruber ,   Bertrand Charpentier

• Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
• Predictive Uncertainty Estimation via Prior Networks
• Posterior Network: Uncertainty Estimation without OOD samples via Density-based Pseudo-Counts
• Evidential Deep Learning to Quantify Classification Uncertainty
• Weight Uncertainty in Neural Networks

Hierarchies in Deep Learning

Type:  Master's Thesis / Guided Research

Multi-scale structures are ubiquitous in real life datasets. As an example, phylogenetic nomenclature naturally reveals a hierarchical classification of species based on their historical evolutions. Learning multi-scale structures can help to exhibit natural and meaningful organizations in the data and also to obtain compact data representation. The goal of this project is to leverage multi-scale structures to improve speed, performances and understanding of Deep Learning models.

Contact: Marcel Kollovieh , Bertrand Charpentier

• Tree Sampling Divergence: An Information-Theoretic Metricfor Hierarchical Graph Clustering
• Hierarchical Graph Representation Learning with Differentiable Pooling
• Gradient-based Hierarchical Clustering
• Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space

Machine Learning - CMU

PhD Dissertations

[all are .pdf files].

Learning Models that Match Jacob Tyo, 2024

Improving Human Integration across the Machine Learning Pipeline Charvi Rastogi, 2024

Reliable and Practical Machine Learning for Dynamic Healthcare Settings Helen Zhou, 2023

Automatic customization of large-scale spiking network models to neuronal population activity (unavailable) Shenghao Wu, 2023

Estimation of BVk functions from scattered data (unavailable) Addison J. Hu, 2023

Rethinking object categorization in computer vision (unavailable) Jayanth Koushik, 2023

Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023

The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness Nil-Jana Akpinar, 2023

Collaborative learning by leveraging siloed data Sebastian Caldas, 2023

Modeling Epidemiological Time Series Aaron Rumack, 2023

Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023

Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023

Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023

Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023

Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023

Applied Mathematics of the Future Kin G. Olivares, 2023

METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023

NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023

Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023

Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023

Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022

Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022

Making Scientific Peer Review Scientific Ivan Stelmakh, 2022

Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022

Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022

Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022

Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022

Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022

Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022

Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022

Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021

Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021

Structure and time course of neural population activity during learning Jay Hennig, 2021

Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021

Meta Reinforcement Learning through Memory Emilio Parisotto, 2021

Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021

Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021

Statistical Game Theory Arun Sai Suggala, 2021

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021

Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021

Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021

Curriculum Learning Otilia Stretcu, 2021

Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021

Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021

Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021

Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021

Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021

Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020

Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020

Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020

Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020

Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020

Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020

Learning DAGs with Continuous Optimization Xun Zheng, 2020

Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020

Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020

Towards Data-Efficient Machine Learning Qizhe Xie, 2020

Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020

Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020

Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020

Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020

Towards Efficient Automated Machine Learning Liam Li, 2020

LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020

Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020

Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020

Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020

Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019

Estimating Probability Distributions and their Properties Shashank Singh, 2019

Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019

Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019

Multi-view Relationships for Analytics and Inference Eric Lei, 2019

Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019

Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019

The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019

Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019

Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019

Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019

Unified Models for Dynamical Systems Carlton Downey, 2019

Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019

Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019

Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019

Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019

Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019

Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019

Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018

Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018

Statistical Inference for Geometric Data Jisu Kim, 2018

Representation Learning @ Scale Manzil Zaheer, 2018

Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018

Distribution and Histogram (DIsH) Learning Junier Oliva, 2018

Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018

Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018

Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018

Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018

Learning with Staleness Wei Dai, 2018

Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017

Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017

New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017

Active Search with Complex Actions and Rewards Yifei Ma, 2017

Why Machine Learning Works George D. Montañez , 2017

Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017

Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016

Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016

Combining Neural Population Recordings: Theory and Application William Bishop, 2015

Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015

Learning Statistical Features of Scene Images Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos Bin Zhao, 2014

Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013

On Learning from Collective Data Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles Liu Yang, 2013

Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013

Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012

Target Sequence Clustering Benjamin Shih, 2011

Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010

Rare Category Analysis Jingrui He, 2010

Coupled Semi-Supervised Learning Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009

Detecting Patterns of Anomalies Kaustav Das, 2009

Dynamics of Large Networks Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008

Stacked Graphical Learning Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007

Tools for Graph Mining Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

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master-thesis

Here are 87 public repositories matching this topic..., johnmartinsson / bird-species-classification.

Using convolutional neural networks to build and train a bird species classifier on bird song data with corresponding species labels.

• Updated Oct 11, 2023

Tim-HW / HW-BlueRov2-Sonar-based-SLAM

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meurissemax / autonomous-drone

Master's thesis about autonomous navigation of a drone in indoor environments carried out to obtain the degree of Master of Science in Computer Science Engineering (University of Liège, academic year 2020-2021).

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Masters Thesis - Fintech Dashboard

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Code for my master thesis in Deep Learning: "Generating answers to medical questions using recurrent neural networks"

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Toolset to analyze disks encrypted with McAFee FDE technology

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harshildarji / thesis

Master's thesis, Uni Passau

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Boren / MasterThesis

Deep Convolutional Neural Networks for Semantic Segmentation of Multi-Band Satellite Images

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thomasSve / Msc_Multi_label_ZeroShot

Code for master thesis on Zero-Shot Learning in multi-label scenarios

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kdevo / chaos-rrs

Chaos - a first of its kind framework for researching Reciprocal Recommender Systems (RRS).

• Updated Nov 7, 2021

JanPokorny / speed-climbing-mapping

Mapping videos of speed climbers onto a virtual wall using ML, OpenCV, and maths. Implementation of my master's thesis.

• Updated Jul 20, 2023

danielathome19 / Form-NN

Master thesis project - a hybrid Neural Network-Decision Tree system and dataset for classical music form recognition and analysis.

• Updated Dec 9, 2022

lukaselmer / hierarchical-paragraph-vectors

Hierarchical Paragraph Vectors

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KyleOng / starreco

State-of-The-Art Rating-based RECOmmendation system: pytorch lightning implementation

• Updated Oct 10, 2023

lcebear / memoryDialogueBot

Master Thesis Project: A memory based dialogue agent

• Updated Dec 20, 2020

develooper1994 / MasterThesis

My Master Thesis experimentation source codes

• Updated Apr 6, 2021

jrmak / FNNR-ABM-Primate

An agent-based model (with a web simulation) for Guizhou "golden" monkey population and movements using the Mesa Python framework; thesis project + human/GTGP expansion; Summer 2018-Winter 2019

• Updated Mar 22, 2020

rand-asswad / muallef

Study of Music Information Retrieval (MIR) methods for multi-pitch estimation and onset detection.

• Updated Dec 8, 2022

EivindArvesen / master_code

Various code from my master's project

• Updated Jan 9, 2019

Josef-Djarf / sEMG-Sim

Source code for multiple parameter modelling of synthetic electromyography data.

• Updated Feb 21, 2024

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Writing a Thesis

Finding a thesis topic.

Students who are interested in writing a bachelor’s or master’s thesis should begin thinking about possible topics (cf. hot topics for thesis projects on this page ) or propose their own (cf. introduction to IML ). Good research questions often have their origins in scientific papers around the research topics of the IML lab. Be on the look out for new data sources that might help provide new insights into a special IML research topic.

Your Advisor and Your Committee

In order to write a bachelor’s or master’s thesis you must find an member of the IML lab who is willing to be your thesis advisor. You propose your thesis topic together with your advisor to Prof. Sonntag as the first reviewer in your committee. 

How Long Should it Be? How Long Does it Take?

A bachelor’s thesis is generally 30-60 pages, not including the bibliography. A master’s thesis is generally 60-80 pages, not including the bibliography. However, the length will vary according to the topic and the method of analysis, so the appropriate length will be determined by you, your advisor, and your committee.  Students who write a master’s thesis generally do so over two semesters, bachelor’s one semester.

Procedure and Formal Requirements

• You are a student at Oldenburg University, follow these instructions .
• You are a student at Saarland University, follow these instructions .

You must maintain continuous enrollment Oldenburg University or at Saarland University while working on the bachelor’s or master’s thesis. If you are planning to conduct interviews, surveys or do other research involving human subjects, you must obtain prior approval from DFKI.

Here you can find some theses examples .

Here you can find project group examples .

Hot Topics for Thesis Projects

You will implement novel modern approaches in computer vision such as Transfer Learning, Graph Neural Network, or Semi-Supervised Learning to solve important medical decision problems like Breast cancer detection, Chest-(X-Ray/CT) abnormalities diagnosis, or related medical domains. The target is to achieve state-of-the-art performance and the proposed method could be explainable to end users to improve the system’s reliability.

Nguyen, Duy MH, et al. “An Attention Mechanism using Multiple Knowledge Sources for COVID-19 Detection from CT Images.”,  AAAI 2021, Workshop: Trustworthy AI for Healthcare. 

Soberanis-Mukul, Roger D., Nassir Navab, and Shadi Albarqouni. “An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation.” arXiv preprint arXiv:2012.03352 (2020).

Contact: Duy Nguyen

In this topic, we will investigate important theoretical machine learning problems that have high impacts on several medical applications. It includes but is not limited to optimization formulation to incorporate efficient user’s feedback to boost the performance of trained models besides available training data (active learning), investigate benefits of transfer learning strategies when dealing with scarce data issues in medical problems, or training algorithms to adapt with highly imbalanced data distribution.

Wilder, Bryan, Eric Horvitz, and Ece Kamar. “Learning to complement humans.” arXiv preprint arXiv:2005.00582 (2020).

De, Abir, et al. “Classification Under Human Assistance.” AAAI (2021).

Yao, Huaxiu, et al. “Hierarchically structured meta-learning.” International Conference on Machine Learning. PMLR, 2019.

Requirements: Programming in Python, ideally experience with processing video and audio data

Project description: The aim is to create an annotated dataset of human-to-human dialogue in Youtube cooking videos*, that can serve as a resource for training ML models to generate conversational explanations of the cooking process. This involves the identification of videos with multiple speakers, speaker diarization (partitioning audio and/or transcript according to speaker identity), identification of conversational interaction between the speakers, and investigating if these interactions qualify as ‘conversational explanations’ of the video content

Contact: Mareike Hartmann

Relevant literature:

Speaker diarization: https://arxiv.org/pdf/2101.09624.pdf Potential videos: http://youcook2.eecs.umich.edu/explore Background on ‘conversational explanations’ from an XAI perspective: https://arxiv.org/pdf/1706.07269.pdf (Sec. 5) Note that in this project, we focus on ‘explaining’ the video content rather than model predictions.

*We focus on the process of cooking as there is some related ongoing work at DFKI, but other instructional scenarios are possible.

Requirements: Programming in Python, Pytorch, basic understanding of Deep Learning, ideally some project work on DL / CV / NLP

Project description: The student will experiment with Image Captioning, more specifically testing existing architectures on different datasets. Then, an error analysis can be conducted, in order to find out how the system can be improved.

Contact: Aliki Anagnostopoulou

• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• From Show to Tell: A Survey on Deep Learning-based Image Captioning

Requirements: Programming in Python, Pytorch (or Tensorflow)

Project description: The aim of the project is to investigate how explainable NMT methods are. For example, attention weights from the Transformer architecture can be used as alignments, however it is not straight-forward which weights can be used.

• Jointly Learning to Align and Translate with Transformer Models
• Accurate Word Alignment Induction from Neural Machine Translation

Project description: The aim of the project is to investigate active learning strategies applied to relation extraction from clinical documents when using deep learning models.

Contact: Siting Liang

• BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction
• Crowdsourcing Ground Truth for Medical Relation Extraction
• Active Learning for Interactive Relation Extraction in a French Newspaper’s Articles

A central finding of preliminary research reveals that different neural network architectures, when trained on the same data distribution, generate diverse attribution maps for local explanations, supporting the assertion that attribution maps are model-dependent [2]. However, it is also understood that these attribution maps, despite their varying origins, can embody certain common characteristics [1].

Given this premise, the proposition for future research is to delve into the development of a novel algorithm that seeks to create attribution maps universally accepted by all models. These models, despite possessing diverse architectures, are based on the same data distribution. This line of enquiry will pave the way towards generating explanations that are devoid of model-dependency or model-bias, thereby privileging model-invariance.

This research aims to bridge the gap between differing neural network architectures, fostering improved communication, data interpretation, and usability. Ultimately, advancements in this field have the potential to significantly propel the evolution of explainable Artificial Intelligence (AI).

Contact: Md Abdul Kadir

[1] Kadir, M. A., Addluri, G. K., & Sonntag, D. (2023). Harmonizing Feature Attributions Across Deep Learning Architectures: Enhancing Interpretability and Consistency.  arXiv preprint arXiv:2307.02150 .

[2] Gupta, A., Saunshi, N., Yu, D., Lyu, K., & Arora, S. (2022). New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound.  Advances in Neural Information Processing Systems ,  35 , 33120-33133.

Colposcopy, a vital method for the diagnosis of cervical pathology, hinges primarily on the visual cues to detect abnormalities and designate regions for biopsies. The conventional method often includes the use of Acetic acid (5%) for highlighting the cells’ nucleus and hence revealing abnormal or pre-cancerous cells, while green filters aid in visualizing blood vessels supplying these regions. However, vast variations in individual practitioner’s experience and expertise may lead to ununiformed assessments.

This research proposal aims to bridge this gap introducing deep learning algorithms, which have shown unprecedented success in image recognition and classification tasks, into colposcopic examinations [1]. The utilization of these machine learning methodologies could allow automatic detection of cancerous or precancerous regions in colposcopic images or videos, automating and standardizing the evaluation process while offering real-time feedback and suggestions during the examination.

[1] Chandran V, Sumithra MG, Karthick A, George T, Deivakani M, Elakkiya B, Subramaniam U, Manoharan S. Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images. Biomed Res Int. 2021 May 4;2021:5584004. doi: 10.1155/2021/5584004. PMID: 33997017; PMCID: PMC8112909.

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17 Compelling Machine Learning Ph.D. Dissertations

Machine Learning Modeling Research posted by Daniel Gutierrez, ODSC August 12, 2021 Daniel Gutierrez, ODSC

Working in the field of data science, I’m always seeking ways to keep current in the field and there are a number of important resources available for this purpose: new book titles, blog articles, conference sessions, Meetups, webinars/podcasts, not to mention the gems floating around in social media. But to dig even deeper, I routinely look at what’s coming out of the world’s research labs. And one great way to keep a pulse for what the research community is working on is to monitor the flow of new machine learning Ph.D. dissertations. Admittedly, many such theses are laser-focused and narrow, but from previous experience reading these documents, you can learn an awful lot about new ways to solve difficult problems over a vast range of problem domains. 

In this article, I present a number of hand-picked machine learning dissertations that I found compelling in terms of my own areas of interest and aligned with problems that I’m working on. I hope you’ll find a number of them that match your own interests. Each dissertation may be challenging to consume but the process will result in hours of satisfying summer reading. Enjoy!

Please check out my previous data science dissertation round-up article . 

1. Fitting Convex Sets to Data: Algorithms and Applications

This machine learning dissertation concerns the geometric problem of finding a convex set that best fits a given data set. The overarching question serves as an abstraction for data-analytical tasks arising in a range of scientific and engineering applications with a focus on two specific instances: (i) a key challenge that arises in solving inverse problems is ill-posedness due to a lack of measurements. A prominent family of methods for addressing such issues is based on augmenting optimization-based approaches with a convex penalty function so as to induce a desired structure in the solution. These functions are typically chosen using prior knowledge about the data. The thesis also studies the problem of learning convex penalty functions directly from data for settings in which we lack the domain expertise to choose a penalty function. The solution relies on suitably transforming the problem of learning a penalty function into a fitting task; and (ii) the problem of fitting tractably-described convex sets given the optimal value of linear functionals evaluated in different directions.

2. Structured Tensors and the Geometry of Data

This machine learning dissertation analyzes data to build a quantitative understanding of the world. Linear algebra is the foundation of algorithms, dating back one hundred years, for extracting structure from data. Modern technologies provide an abundance of multi-dimensional data, in which multiple variables or factors can be compared simultaneously. To organize and analyze such data sets we can use a tensor , the higher-order analogue of a matrix. However, many theoretical and practical challenges arise in extending linear algebra to the setting of tensors. The first part of the thesis studies and develops the algebraic theory of tensors. The second part of the thesis presents three algorithms for tensor data. The algorithms use algebraic and geometric structure to give guarantees of optimality.

3. Statistical approaches for spatial prediction and anomaly detection

This machine learning dissertation is primarily a description of three projects. It starts with a method for spatial prediction and parameter estimation for irregularly spaced, and non-Gaussian data. It is shown that by judiciously replacing the likelihood with an empirical likelihood in the Bayesian hierarchical model, approximate posterior distributions for the mean and covariance parameters can be obtained. Due to the complex nature of the hierarchical model, standard Markov chain Monte Carlo methods cannot be applied to sample from the posterior distributions. To overcome this issue, a generalized sequential Monte Carlo algorithm is used. Finally, this method is applied to iron concentrations in California. The second project focuses on anomaly detection for functional data; specifically for functional data where the observed functions may lie over different domains. By approximating each function as a low-rank sum of spline basis functions the coefficients will be compared for each basis across each function. The idea being, if two functions are similar then their respective coefficients should not be significantly different. This project concludes with an application of the proposed method to detect anomalous behavior of users of a supercomputer at NREL. The final project is an extension of the second project to two-dimensional data. This project aims to detect location and temporal anomalies from ground motion data from a fiber-optic cable using distributed acoustic sensing (DAS). 

4. Sampling for Streaming Data

Advances in data acquisition technology pose challenges in analyzing large volumes of streaming data. Sampling is a natural yet powerful tool for analyzing such data sets due to their competent estimation accuracy and low computational cost. Unfortunately, sampling methods and their statistical properties for streaming data, especially streaming time series data, are not well studied in the literature. Meanwhile, estimating the dependence structure of multidimensional streaming time-series data in real-time is challenging. With large volumes of streaming data, the problem becomes more difficult when the multidimensional data are collected asynchronously across distributed nodes, which motivates us to sample representative data points from streams. This machine learning dissertation proposes a series of leverage score-based sampling methods for streaming time series data. The simulation studies and real data analysis are conducted to validate the proposed methods. The theoretical analysis of the asymptotic behaviors of the least-squares estimator is developed based on the subsamples.

5.  Statistical Machine Learning Methods for Complex, Heterogeneous Data

This machine learning dissertation develops statistical machine learning methodology for three distinct tasks. Each method blends classical statistical approaches with machine learning methods to provide principled solutions to problems with complex, heterogeneous data sets. The first framework proposes two methods for high-dimensional shape-constrained regression and classification. These methods reshape pre-trained prediction rules to satisfy shape constraints like monotonicity and convexity. The second method provides a nonparametric approach to the econometric analysis of discrete choice. This method provides a scalable algorithm for estimating utility functions with random forests, and combines this with random effects to properly model preference heterogeneity. The final method draws inspiration from early work in statistical machine translation to construct embeddings for variable-length objects like mathematical equations

6. Topics in Multivariate Statistics with Dependent Data

This machine learning dissertation comprises four chapters. The first is an introduction to the topics of the dissertation and the remaining chapters contain the main results. Chapter 2 gives new results for consistency of maximum likelihood estimators with a focus on multivariate mixed models. The presented theory builds on the idea of using subsets of the full data to establish consistency of estimators based on the full data. The theory is applied to two multivariate mixed models for which it was unknown whether maximum likelihood estimators are consistent. In Chapter 3 an algorithm is proposed for maximum likelihood estimation of a covariance matrix when the corresponding correlation matrix can be written as the Kronecker product of two lower-dimensional correlation matrices. The proposed method is fully likelihood-based. Some desirable properties of separable correlation in comparison to separable covariance are also discussed. Chapter 4 is concerned with Bayesian vector auto-regressions (VARs). A collapsed Gibbs sampler is proposed for Bayesian VARs with predictors and the convergence properties of the algorithm are studied. 

7.  Model Selection and Estimation for High-dimensional Data Analysis

In the era of big data, uncovering useful information and hidden patterns in the data is prevalent in different fields. However, it is challenging to effectively select input variables in data and estimate their effects. The goal of this machine learning dissertation is to develop reproducible statistical approaches that provide mechanistic explanations of the phenomenon observed in big data analysis. The research contains two parts: variable selection and model estimation. The first part investigates how to measure and interpret the usefulness of an input variable using an approach called “variable importance learning” and builds tools (methodology and software) that can be widely applied. Two variable importance measures are proposed, a parametric measure SOIL and a non-parametric measure CVIL, using the idea of a model combining and cross-validation respectively. The SOIL method is theoretically shown to have the inclusion/exclusion property: When the model weights are properly around the true model, the SOIL importance can well separate the variables in the true model from the rest. The CVIL method possesses desirable theoretical properties and enhances the interpretability of many mysterious but effective machine learning methods. The second part focuses on how to estimate the effect of a useful input variable in the case where the interaction of two input variables exists. Investigated is the minimax rate of convergence for regression estimation in high-dimensional sparse linear models with two-way interactions, and construct an adaptive estimator that achieves the minimax rate of convergence regardless of the true heredity condition and the sparsity indices.

8.  High-Dimensional Structured Regression Using Convex Optimization

While the term “Big Data” can have multiple meanings, this dissertation considers the type of data in which the number of features can be much greater than the number of observations (also known as high-dimensional data). High-dimensional data is abundant in contemporary scientific research due to the rapid advances in new data-measurement technologies and computing power. Recent advances in statistics have witnessed great development in the field of high-dimensional data analysis. This machine learning dissertation proposes three methods that study three different components of a general framework of the high-dimensional structured regression problem. A general theme of the proposed methods is that they cast a certain structured regression as a convex optimization problem. In so doing, the theoretical properties of each method can be well studied, and efficient computation is facilitated. Each method is accompanied by a thorough theoretical analysis of its performance, and also by an R package containing its practical implementation. It is shown that the proposed methods perform favorably (both theoretically and practically) compared with pre-existing methods.

9. Asymptotics and Interpretability of Decision Trees and Decision Tree Ensembles

Decision trees and decision tree ensembles are widely used nonparametric statistical models. A decision tree is a binary tree that recursively segments the covariate space along the coordinate directions to create hyper rectangles as basic prediction units for fitting constant values within each of them. A decision tree ensemble combines multiple decision trees, either in parallel or in sequence, in order to increase model flexibility and accuracy, as well as to reduce prediction variance. Despite the fact that tree models have been extensively used in practice, results on their asymptotic behaviors are scarce. This machine learning dissertation presents analyses on tree asymptotics in the perspectives of tree terminal nodes, tree ensembles, and models incorporating tree ensembles respectively. The study introduces a few new tree-related learning frameworks which provides provable statistical guarantees and interpretations. A study on the Gini index used in the greedy tree building algorithm reveals its limiting distribution, leading to the development of a test of better splitting that helps to measure the uncertain optimality of a decision tree split. This test is combined with the concept of decision tree distillation, which implements a decision tree to mimic the behavior of a block box model, to generate stable interpretations by guaranteeing a unique distillation tree structure as long as there are sufficiently many random sample points. Also applied is mild modification and regularization to the standard tree boosting to create a new boosting framework named Boulevard. Also included is an integration of two new mechanisms: honest trees , which isolate the tree terminal values from the tree structure, and adaptive shrinkage , which scales the boosting history to create an equally weighted ensemble. This theoretical development provides the prerequisite for the practice of statistical inference with boosted trees. Lastly, the thesis investigates the feasibility of incorporating existing semi-parametric models with tree boosting. 

10. Bayesian Models for Imputing Missing Data and Editing Erroneous Responses in Surveys

This dissertation develops Bayesian methods for handling unit nonresponse, item nonresponse, and erroneous responses in large-scale surveys and censuses containing categorical data. The focus is on applications of nested household data where individuals are nested within households and certain combinations of the variables are not allowed, such as the U.S. Decennial Census, as well as surveys subject to both unit and item nonresponse, such as the Current Population Survey.

11. Localized Variable Selection with Random Forest  

Due to recent advances in computer technology, the cost of collecting and storing data has dropped drastically. This makes it feasible to collect large amounts of information for each data point. This increasing trend in feature dimensionality justifies the need for research on variable selection. Random forest (RF) has demonstrated the ability to select important variables and model complex data. However, simulations confirm that it fails in detecting less influential features in presence of variables with large impacts in some cases. This dissertation proposes two algorithms for localized variable selection: clustering-based feature selection (CBFS) and locally adjusted feature importance (LAFI). Both methods aim to find regions where the effects of weaker features can be isolated and measured. CBFS combines RF variable selection with a two-stage clustering method to detect variables where their effect can be detected only in certain regions. LAFI, on the other hand, uses a binary tree approach to split data into bins based on response variable rankings, and implements RF to find important variables in each bin. Larger LAFI is assigned to variables that get selected in more bins. Simulations and real data sets are used to evaluate these variable selection methods. 

12. Functional Principal Component Analysis and Sparse Functional Regression

The focus of this dissertation is on functional data which are sparsely and irregularly observed. Such data require special consideration, as classical functional data methods and theory were developed for densely observed data. As is the case in much of functional data analysis, the functional principal components (FPCs) play a key role in current sparse functional data methods via the Karhunen-Loéve expansion. Thus, after a review of relevant background material, this dissertation is divided roughly into two parts, the first focusing specifically on theoretical properties of FPCs, and the second on regression for sparsely observed functional data.

13. Essays In Causal Inference: Addressing Bias In Observational And Randomized Studies Through Analysis And Design

In observational studies, identifying assumptions may fail, often quietly and without notice, leading to biased causal estimates. Although less of a concern in randomized trials where treatment is assigned at random, bias may still enter the equation through other means. This dissertation has three parts, each developing new methods to address a particular pattern or source of bias in the setting being studied. The first part extends the conventional sensitivity analysis methods for observational studies to better address patterns of heterogeneous confounding in matched-pair designs. The second part develops a modified difference-in-difference design for comparative interrupted time-series studies. The method permits partial identification of causal effects when the parallel trends assumption is violated by an interaction between group and history. The method is applied to a study of the repeal of Missouri’s permit-to-purchase handgun law and its effect on firearm homicide rates. The final part presents a study design to identify vaccine efficacy in randomized control trials when there is no gold standard case definition. The approach augments a two-arm randomized trial with natural variation of a genetic trait to produce a factorial experiment. 

14. Bayesian Shrinkage: Computation, Methods, and Theory

Sparsity is a standard structural assumption that is made while modeling high-dimensional statistical parameters. This assumption essentially entails a lower-dimensional embedding of the high-dimensional parameter thus enabling sound statistical inference. Apart from this obvious statistical motivation, in many modern applications of statistics such as Genomics, Neuroscience, etc. parameters of interest are indeed of this nature. For over almost two decades, spike and slab type priors have been the Bayesian gold standard for modeling of sparsity. However, due to their computational bottlenecks, shrinkage priors have emerged as a powerful alternative. This family of priors can almost exclusively be represented as a scale mixture of Gaussian distribution and posterior Markov chain Monte Carlo (MCMC) updates of related parameters are then relatively easy to design. Although shrinkage priors were tipped as having computational scalability in high-dimensions, when the number of parameters is in thousands or more, they do come with their own computational challenges. Standard MCMC algorithms implementing shrinkage priors generally scale cubic in the dimension of the parameter making real-life application of these priors severely limited. 

The first chapter of this dissertation addresses this computational issue and proposes an alternative exact posterior sampling algorithm complexity of which that linearly in the ambient dimension. The algorithm developed in the first chapter is specifically designed for regression problems. The second chapter develops a Bayesian method based on shrinkage priors for high-dimensional multiple response regression. Chapter three chooses a specific member of the shrinkage family known as the horseshoe prior and studies its convergence rates in several high-dimensional models. 

15.  Topics in Measurement Error Analysis and High-Dimensional Binary Classification

This dissertation proposes novel methods to tackle two problems: the misspecified model with measurement error and high-dimensional binary classification, both have a crucial impact on applications in public health. The first problem exists in the epidemiology practice. Epidemiologists often categorize a continuous risk predictor since categorization is thought to be more robust and interpretable, even when the true risk model is not a categorical one. Thus, their goal is to fit the categorical model and interpret the categorical parameters. The second project considers the problem of high-dimensional classification between the two groups with unequal covariance matrices. Rather than estimating the full quadratic discriminant rule, it is proposed to perform simultaneous variable selection and linear dimension reduction on original data, with the subsequent application of quadratic discriminant analysis on the reduced space. Further, in order to support the proposed methodology, two R packages were developed, CCP and DAP, along with two vignettes as long-format illustrations for their usage.

16. Model-Based Penalized Regression

This dissertation contains three chapters that consider penalized regression from a model-based perspective, interpreting penalties as assumed prior distributions for unknown regression coefficients. The first chapter shows that treating a lasso penalty as a prior can facilitate the choice of tuning parameters when standard methods for choosing the tuning parameters are not available, and when it is necessary to choose multiple tuning parameters simultaneously. The second chapter considers a possible drawback of treating penalties as models, specifically possible misspecification. The third chapter introduces structured shrinkage priors for dependent regression coefficients which generalize popular independent shrinkage priors. These can be useful in various applied settings where many regression coefficients are not only expected to be nearly or exactly equal to zero, but also structured.

17. Topics on Least Squares Estimation

This dissertation revisits and makes progress on some old but challenging problems concerning least squares estimation, the work-horse of supervised machine learning. Two major problems are addressed: (i) least squares estimation with heavy-tailed errors, and (ii) least squares estimation in non-Donsker classes. For (i), this problem is studied both from a worst-case perspective, and a more refined envelope perspective. For (ii), two case studies are performed in the context of (a) estimation involving sets and (b) estimation of multivariate isotonic functions. Understanding these particular aspects of least squares estimation problems requires several new tools in the empirical process theory, including a sharp multiplier inequality controlling the size of the multiplier empirical process, and matching upper and lower bounds for empirical processes indexed by non-Donsker classes.

How to Learn More about Machine Learning

At our upcoming event this November 16th-18th in San Francisco,  ODSC West 2021  will feature a plethora of talks, workshops, and training sessions on machine learning and machine learning research. You can  register now for 50% off all ticket types  before the discount drops to 40% in a few weeks. Some  highlighted sessions on machine learning  include:

• Towards More Energy-Efficient Neural Networks? Use Your Brain!: Olaf de Leeuw | Data Scientist | Dataworkz
• Practical MLOps: Automation Journey: Evgenii Vinogradov, PhD | Head of DHW Development | YooMoney
• Applications of Modern Survival Modeling with Python: Brian Kent, PhD | Data Scientist | Founder The Crosstab Kite
• Using Change Detection Algorithms for Detecting Anomalous Behavior in Large Systems: Veena Mendiratta, PhD | Adjunct Faculty, Network Reliability and Analytics Researcher | Northwestern University

Sessions on MLOps:

• Tuning Hyperparameters with Reproducible Experiments: Milecia McGregor | Senior Software Engineer | Iterative
• MLOps… From Model to Production: Filipa Peleja, PhD | Lead Data Scientist | Levi Strauss & Co
• Operationalization of Models Developed and Deployed in Heterogeneous Platforms: Sourav Mazumder | Data Scientist, Thought Leader, AI & ML Operationalization Leader | IBM
• Develop and Deploy a Machine Learning Pipeline in 45 Minutes with Ploomber: Eduardo Blancas | Data Scientist | Fidelity Investments

Sessions on Deep Learning:

• GANs: Theory and Practice, Image Synthesis With GANs Using TensorFlow: Ajay Baranwal | Center Director | Center for Deep Learning in Electronic Manufacturing, Inc
• Machine Learning With Graphs: Going Beyond Tabular Data: Dr. Clair J. Sullivan | Data Science Advocate | Neo4j
• Deep Dive into Reinforcement Learning with PPO using TF-Agents & TensorFlow 2.0: Oliver Zeigermann | Software Developer | embarc Software Consulting GmbH
• Get Started with Time-Series Forecasting using the Google Cloud AI Platform: Karl Weinmeister | Developer Relations Engineering Manager | Google

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.

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Guideline for Students writing a Bachelor or Master’s Thesis

In the intelligent systems and machine learning (isml) group.

Status: 16.11.2020

• Finding a thesis topic: Students can choose from our list of open topics or make own suggestions. The time required for finding a topic depends on the student’s efforts and her/his familiarity with the research of the ISML group.
• Writing and submitting the thesis proposal: Writing a thesis proposal is important to specify the topic and define the goals of the thesis. This activity includes familiarization with the topic and normally requires about 2–4 weeks. The time spent for the proposal is not considered as part of the thesis. You will be registered officially upon successful submission of the proposal. 
• Starter talk: Shortly after the start of the thesis, it is common to give a short presentation in the seminar of the research group. The talk is meant to explain the topic and the goals of the thesis, and to sketch some first ideas of how to tackle these goals, so that the members of the group can give comments and provide feedback.
• Working on the thesis: In order to accomplish and successfully complete your thesis, you need to organize your time between reading relevant literature and related work, solving your own tasks, developing and implementing your solutions, and writing the thesis. As a research group, we offer you support through regular meetings with the supervisor, our students seminar, a suitable working environment, and proofreading before the official submission of the thesis.
• Submitting the thesis: At this stage, you are required to submit hard copies of the written thesis, supplemented with your source code, experimental results, a PDF of the thesis, etc., in electronic from.
• Oral defense: The defense will take place after the formal submission. Defending the thesis prior to the formal submission is only possible under specific circumstances and in agreement with the supervisors.
• Prerequisites Students interested in writing a thesis in our group should have a background in our main research topics. Normally, we assume that you attended (and successfully passed) some of our lectures (Foundations of Intelligent Systems or Data Mining in the case of Bachelor students, and Machine Learning I + II in the case of Master students). Exceptions to this rule are possible but need to be justified.
• Topics A list of open topics for Bachelor and Master’s theses can be found on the ISML website. Please have a look at this list prior to contacting Prof. Hüllermeier or any other group member (contact persons are also indicated on each thesis proposal). Ideally, you preselect two or three topics according to your preferences, and then ask for an appointment to discuss about details. Needless to say, you are also invited to propose topics yourself. In this case, please prepare a one-page summary of your main ideas.
• Supervision and co-supervision Prof. Hüllermeier will be the main supervisor for all theses written in the ISML group. Additionally, you will be assigned one of the research assistants as a co-supervisor and primary contact person. Please make sure that you meet and/or communicate with the co-supervisor on a regular basis, so as to keep her or him informed about the progress as well as any kind of difficulties that may arise. As a rough guideline, you should get in touch with co-supervisor at least every two weeks. Students are encouraged to organize their work and share it with the supervisor using the university’s GitLab service; in this way, sending emails with large attachments can also be avoided.
• Proposal Once the topic has been fixed, a proposal for the thesis needs to be prepared. This proposal is a prerequisite for registering the thesis. It is supposed to provide a motivation of the topic, to elaborate on the state of the art and prior work, and to explicate the goals of the thesis. The goals should be defined carefully, as they provide the basis for the final assessment of the thesis. The proposal must also contain a preliminary structure of the thesis and a schedule (Gantt chart). The preparation of an acceptable proposal—with appropriate assistance and in due course—is a first contribution and a main prerequisite for registration of the thesis. We reserve the right to withdraw our commitment of supervision in cases where this prerequisite is not met.
• Template The thesis should be written in LaTex and adhere to our template .
• Proofreading The co-supervisor will normally offer you to proofread a draft of your thesis, either as a whole or in parts (e.g., single chapters). The goal of proofreading is to provide general feedback and give hints for possible improvements (but not to make detailed corrections). Please note that proofreading will be offered only once for each part of the thesis; therefore, you should make sure that your draft is sufficiently mature. Moreover, please take into consideration that feedback cannot be expected on a very short notice; typically, it will take about two weeks.
• Working environment You are kindly invited to work in the PC pool of the ISML group, unless space restrictions prevent us from offering you a place equipped with a computer. Of course, if you prefer, you can also work at home or any other place. In special cases, we can support you in acquiring a virtual machine from the IMT , or to get access to HPC resources (including GPUs).
• Oral defense The thesis will end with an oral defence in the form of a seminar. The seminar will start with a presentation of the topic and main results of the thesis, followed by an informal discussion in which the audience can ask questions. The presentation should not exceed 20 minutes in the case of Bachelor and 25 minutes in the case of Master’s theses. There is no time limit for discussion.
• Working attitude Familiarization with the topic; independency, autonomy; commitment and engagement; utilization of resources (tools, computer infrastructure, etc.).
• Contents and results Documentation of related work; solution of the tasks and compliance with objectives; quality of the solutions; documentation und reproducibility.
• Written thesis Structure and readability of the thesis; length of the thesis, balance between breadth and depth; formal presentation and correctness; linguistic quality (grammar, orthography, typos, etc.); extent and quality of illustrations, figures, tables, etc.; citation and list of references (coverage, correctness, etc.).
• Defense of the thesis Quality of presentation; quality of slides; response to questions; compliance with time limit.

Further information:

• Open Theses
• Proposal Template
• Thesis Template

The Future of AI Research: 20 Thesis Ideas for Undergraduate Students in Machine Learning and Deep Learning for 2023!

A comprehensive guide for crafting an original and innovative thesis in the field of ai..

By Aarafat Islam

“The beauty of machine learning is that it can be applied to any problem you want to solve, as long as you can provide the computer with enough examples.” — Andrew Ng

This article provides a list of 20 potential thesis ideas for an undergraduate program in machine learning and deep learning in 2023. Each thesis idea includes an  introduction , which presents a brief overview of the topic and the  research objectives . The ideas provided are related to different areas of machine learning and deep learning, such as computer vision, natural language processing, robotics, finance, drug discovery, and more. The article also includes explanations, examples, and conclusions for each thesis idea, which can help guide the research and provide a clear understanding of the potential contributions and outcomes of the proposed research. The article also emphasized the importance of originality and the need for proper citation in order to avoid plagiarism.

1. Investigating the use of Generative Adversarial Networks (GANs) in medical imaging:  A deep learning approach to improve the accuracy of medical diagnoses.

Introduction:  Medical imaging is an important tool in the diagnosis and treatment of various medical conditions. However, accurately interpreting medical images can be challenging, especially for less experienced doctors. This thesis aims to explore the use of GANs in medical imaging, in order to improve the accuracy of medical diagnoses.

2. Exploring the use of deep learning in natural language generation (NLG): An analysis of the current state-of-the-art and future potential.

Introduction:  Natural language generation is an important field in natural language processing (NLP) that deals with creating human-like text automatically. Deep learning has shown promising results in NLP tasks such as machine translation, sentiment analysis, and question-answering. This thesis aims to explore the use of deep learning in NLG and analyze the current state-of-the-art models, as well as potential future developments.

3. Development and evaluation of deep reinforcement learning (RL) for robotic navigation and control.

Introduction:  Robotic navigation and control are challenging tasks, which require a high degree of intelligence and adaptability. Deep RL has shown promising results in various robotics tasks, such as robotic arm control, autonomous navigation, and manipulation. This thesis aims to develop and evaluate a deep RL-based approach for robotic navigation and control and evaluate its performance in various environments and tasks.

4. Investigating the use of deep learning for drug discovery and development.

Introduction:  Drug discovery and development is a time-consuming and expensive process, which often involves high failure rates. Deep learning has been used to improve various tasks in bioinformatics and biotechnology, such as protein structure prediction and gene expression analysis. This thesis aims to investigate the use of deep learning for drug discovery and development and examine its potential to improve the efficiency and accuracy of the drug development process.

5. Comparison of deep learning and traditional machine learning methods for anomaly detection in time series data.

Introduction:  Anomaly detection in time series data is a challenging task, which is important in various fields such as finance, healthcare, and manufacturing. Deep learning methods have been used to improve anomaly detection in time series data, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for anomaly detection in time series data and examine their respective strengths and weaknesses.

Photo by  Joanna Kosinska  on  Unsplash

6. Use of deep transfer learning in speech recognition and synthesis.

Introduction:  Speech recognition and synthesis are areas of natural language processing that focus on converting spoken language to text and vice versa. Transfer learning has been widely used in deep learning-based speech recognition and synthesis systems to improve their performance by reusing the features learned from other tasks. This thesis aims to investigate the use of transfer learning in speech recognition and synthesis and how it improves the performance of the system in comparison to traditional methods.

7. The use of deep learning for financial prediction.

Introduction:  Financial prediction is a challenging task that requires a high degree of intelligence and adaptability, especially in the field of stock market prediction. Deep learning has shown promising results in various financial prediction tasks, such as stock price prediction and credit risk analysis. This thesis aims to investigate the use of deep learning for financial prediction and examine its potential to improve the accuracy of financial forecasting.

8. Investigating the use of deep learning for computer vision in agriculture.

Introduction:  Computer vision has the potential to revolutionize the field of agriculture by improving crop monitoring, precision farming, and yield prediction. Deep learning has been used to improve various computer vision tasks, such as object detection, semantic segmentation, and image classification. This thesis aims to investigate the use of deep learning for computer vision in agriculture and examine its potential to improve the efficiency and accuracy of crop monitoring and precision farming.

9. Development and evaluation of deep learning models for generative design in engineering and architecture.

Introduction:  Generative design is a powerful tool in engineering and architecture that can help optimize designs and reduce human error. Deep learning has been used to improve various generative design tasks, such as design optimization and form generation. This thesis aims to develop and evaluate deep learning models for generative design in engineering and architecture and examine their potential to improve the efficiency and accuracy of the design process.

10. Investigating the use of deep learning for natural language understanding.

Introduction:  Natural language understanding is a complex task of natural language processing that involves extracting meaning from text. Deep learning has been used to improve various NLP tasks, such as machine translation, sentiment analysis, and question-answering. This thesis aims to investigate the use of deep learning for natural language understanding and examine its potential to improve the efficiency and accuracy of natural language understanding systems.

Photo by  UX Indonesia  on  Unsplash

11. Comparing deep learning and traditional machine learning methods for image compression.

Introduction:  Image compression is an important task in image processing and computer vision. It enables faster data transmission and storage of image files. Deep learning methods have been used to improve image compression, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for image compression and examine their respective strengths and weaknesses.

12. Using deep learning for sentiment analysis in social media.

Introduction:  Sentiment analysis in social media is an important task that can help businesses and organizations understand their customers’ opinions and feedback. Deep learning has been used to improve sentiment analysis in social media, by training models on large datasets of social media text. This thesis aims to use deep learning for sentiment analysis in social media, and evaluate its performance against traditional machine learning methods.

13. Investigating the use of deep learning for image generation.

Introduction:  Image generation is a task in computer vision that involves creating new images from scratch or modifying existing images. Deep learning has been used to improve various image generation tasks, such as super-resolution, style transfer, and face generation. This thesis aims to investigate the use of deep learning for image generation and examine its potential to improve the quality and diversity of generated images.

14. Development and evaluation of deep learning models for anomaly detection in cybersecurity.

Introduction:  Anomaly detection in cybersecurity is an important task that can help detect and prevent cyber-attacks. Deep learning has been used to improve various anomaly detection tasks, such as intrusion detection and malware detection. This thesis aims to develop and evaluate deep learning models for anomaly detection in cybersecurity and examine their potential to improve the efficiency and accuracy of cybersecurity systems.

15. Investigating the use of deep learning for natural language summarization.

Introduction:  Natural language summarization is an important task in natural language processing that involves creating a condensed version of a text that preserves its main meaning. Deep learning has been used to improve various natural language summarization tasks, such as document summarization and headline generation. This thesis aims to investigate the use of deep learning for natural language summarization and examine its potential to improve the efficiency and accuracy of natural language summarization systems.

Photo by  Windows  on  Unsplash

16. Development and evaluation of deep learning models for facial expression recognition.

Introduction:  Facial expression recognition is an important task in computer vision and has many practical applications, such as human-computer interaction, emotion recognition, and psychological studies. Deep learning has been used to improve facial expression recognition, by training models on large datasets of images. This thesis aims to develop and evaluate deep learning models for facial expression recognition and examine their performance against traditional machine learning methods.

17. Investigating the use of deep learning for generative models in music and audio.

Introduction:  Music and audio synthesis is an important task in audio processing, which has many practical applications, such as music generation and speech synthesis. Deep learning has been used to improve generative models for music and audio, by training models on large datasets of audio data. This thesis aims to investigate the use of deep learning for generative models in music and audio and examine its potential to improve the quality and diversity of generated audio.

18. Study the comparison of deep learning models with traditional algorithms for anomaly detection in network traffic.

Introduction:  Anomaly detection in network traffic is an important task that can help detect and prevent cyber-attacks. Deep learning models have been used for this task, and traditional methods such as clustering and rule-based systems are widely used as well. This thesis aims to compare deep learning models with traditional algorithms for anomaly detection in network traffic and analyze the trade-offs between the models in terms of accuracy and scalability.

19. Investigating the use of deep learning for improving recommender systems.

Introduction:  Recommender systems are widely used in many applications such as online shopping, music streaming, and movie streaming. Deep learning has been used to improve the performance of recommender systems, by training models on large datasets of user-item interactions. This thesis aims to investigate the use of deep learning for improving recommender systems and compare its performance with traditional content-based and collaborative filtering approaches.

20. Development and evaluation of deep learning models for multi-modal data analysis.

Introduction:  Multi-modal data analysis is the task of analyzing and understanding data from multiple sources such as text, images, and audio. Deep learning has been used to improve multi-modal data analysis, by training models on large datasets of multi-modal data. This thesis aims to develop and evaluate deep learning models for multi-modal data analysis and analyze their potential to improve performance in comparison to single-modal models.

I hope that this article has provided you with a useful guide for your thesis research in machine learning and deep learning. Remember to conduct a thorough literature review and to include proper citations in your work, as well as to be original in your research to avoid plagiarism. I wish you all the best of luck with your thesis and your research endeavors!

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Bachelor and Master Thesis Opportunities

Finding a supervisor.

Our group supervises as many students as possible, but we often do not have the capacity to supervise all interested students.

Preliminaries

Prerequisites for thesis candidates.

Applicants must have significant experience in machine learning, e.g., as acquired through courses offered by our group (passed with a grade of “good” or better) or an equivalent qualification. This often includes a deep conceptual understanding of machine learning and significant programming experience. The necessary skills vary depending on the thesis topic, e.g., purely theoretical theses require more mathematical than programming skills. Candidates must possess the necessary abilities before applying. Cooperation with an external party (like a company or research institute) is usually only possible when the collaborating party does not significantly reduce scientific autonomy (e.g., regarding data access, topic selection, and thesis trajectory).

Application form

Interested students can contact us with the thesis application form as a PDF, a curriculum vitae or resume, and a cover letter. Please provide evidence of relevant skills. Degree theses are often connected to ongoing research in our group. However, it is also possible for students to suggest their own topics/ideas.

Application channels

1. contacting researchers directly.

Candidates can apply to potential supervisors directly using the standardized form above. In your email, please detail your reasons for reaching out to the particular individual.

How to find suitable researchers:

• Filter researchers by interests using this platform .
• Browse our team overview and research profile .
• Contact the researchers from the section “Thesis supervision offerings” below.

Students may send a follow-up email if a researcher does not respond, as messages can sometimes be overlooked amidst numerous emails. Please wait at least two weeks before writing such a follow-up mail.

2. Applying via a mailing list

In addition to targeted applications via channel (1), candidates can send their application to thesis(∂)ml.tu-berlin.de using the standardized form above. This distribution list reaches most researchers in our group. Responses will be provided only if your application aligns well with the needs of at least one researcher in our group.

3. Contacting the Thesis Coordination Team

If previous application attempts via the outlined channels are unsuccessful, and you meet the necessary thesis candidate prerequisites, you are encouraged to contact the Thesis Domain Coordinators. Please include a brief summary of your prior application attempts in your communication.

Thesis Domain Coordinators:

• Quantum Chemistry / ML for Physical Sciences: Stefan Chmiela
• Explainable AI: Grégoire Montavon
• Probabilistic ML: Shinichi Nakajima
• Biomedical Sensing and Neurotechnology: Alexander von Lühmann
• Kernel Methods: Andreas Ziehe
• Computational Neuroscience: Mina Jamshidi Idaji
• Digital Pathology: Julius Hense
• Multimodal Learning: Jannik Wolff
• Anomaly Detection: Alexander Bauer
• NLP and Digital Humanities: Oliver Eberle
• Graph Neural Networks: Thomas Schnake

For topics not covered by the above domains, or for general inquiries and feedback regarding the application process, please direct your correspondence to thesis.coordination(∂)ml.tu-berlin.de. If students do not receive a reply from the Thesis Domain Coordinators or the mailing list, they may send a follow-up email after a waiting period of at least two weeks.

Thesis supervision offerings

Please be aware that we often keep the titles of available theses confidential, as they frequently pertain to our current, yet-to-be-published research. Group members can access our internal database .

High-level overview of inquiring researchers

Publicly advertised thesis topics, next steps after having found a supervisor.

General information (link)

After having found a supervisor, students prepare a thesis proposal that includes

• the research question and its context/motivation,
• related work,
• preliminary methodological and/or experimental results,
• and formalities such as the number of ECTS credits and the writing time specified in the student’s examination regulations.

The supervisor can help the student with writing the proposal. Students may register their thesis with the examination office once Prof. Müller or an authorized senior researcher approves the proposal. We encourage students not to underestimate the time required for writing the proposal. Furthermore, consider that we may require some time to review the proposal. Therefore, it is helpful to apply to our group early.

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Machine Learning - Master

Key data on study program.

Faculty Science

Academic Degree Master

Restricted admission None

Regular duration of studies 4 semester

Is a combination subject required? No

Application deadline for Germans and EU citizens  Winter semester: 30.04

Application deadline for non-EU citizens Winter semester: 30.04

Valid for all applicants with German or EU university entrance qualification.

Program start Winter semester

Language of instruction English

Costs Tuition and other fees

Requirements and application

On what criteria are applicants selected.

Admission to the international Master’s program in Machine Learning is not admission-restricted, but all students, including those holding a Bachelor’s degree in Computer Science, are requested to submit a formal application. However, you need to apply  in order to establish whether you meet the general requirements.

Application deadline for international (non-EU) citizens as well as German and EU citizens: 30 April

Further information and answers to the most common questions can be found on the website of the Master's program and on the FAQ page .

Which requirements must I meet for studies?

A Bachelor's degree (or equivalent) in either computer science, mathematics, physics or related natural sciences with a grade of at least 2.3 (German grading system). In particular, competences from the following areas are required, equivalent in content and scope to those in the BSc course in Computer Science in Tübingen:

Mathematics: one- and multidimensional analysis, linear algebra and either numerics or stochastic

Computer science: Programming, Algorithms and Data Structures.

Applicants must also provide adequate proof of English proficiency.

Further information & FAQ regarding the admission requirements

Which languages must I know?

Courses are held in English. Applicants must provide adequate proof of English proficiency, documented by one of the following documents:

German Abitur certificate with proof of 6 (G8) or 7 years (G9) of English language instruction

TOEFL iBT test with at least 94 points

IELTS test with a score of at least 7.0

Cambridge Certificate in Advanced English (CAE)

Higher education entrance qualification from Great Britain, Ireland, USA, Canada, Australia, New Zealand

Which other subjects can I combine with my main subject?

The Machine Learning program cannot be complemented with a minor subject.

Program details

Which topics are dealt with in the program.

The international Master’s program in machine learning offers a wide choice of courses in computer science and, apart from a few mandatory courses, allows students to choose their subjects according to interest. Students will attend lectures, seminars, and project lab courses under the supervision of scientists who introduce them to basic and applied research and current topics in machine learning.

To pick up on scientific trends and make the best use of the current state of research, the curriculum relies heavily on the strong research presence on site, in machine learning as well as the wider field of computer science: top-level researchers in all major methodological branches of machine learning are present in Tübingen – personnel that will actively engage in teaching for the Master’s Program Machine Learning. Since the field is obviously very young and currently developing extremely rapidly, training will naturally be based on the most recent insights and the most pressing research questions of these teaching researchers. Project work and the Master’s thesis will offer students the opportunity to develop code for research purposes and their own scientific projects.

As interdisciplinarity is an important aspect, the Master’s thesis can be supervised by a professor from any subfield of computer science.

What is special about the Tübingen program?

The University of Tuebingen collaborates closely with the  Max Planck Institute for Intelligent Systems and the Max Planck Institute for Biological Cybernetics and they form together one of leading research hubs for machine learning in Europe and world-wide. This manifests in several recent initiatives:

the Cyber Valley ,

the Excellence Cluster `Machine Learning: New Perspectives for Science' ,

the TUE AI Center (BMBF Competence Center for Machine Learning)

the International Max Planck Research School for Intelligent Systems .

These initatives provide plenty of opportunities for research as well as industry contacts for the students of our master program.

How is the program structured?

The course consists of four major study areas: Foundations of Machine Learning, Diverse Topics in Machine Learning, General Computer Science and Expanded Perspectives. In the first semester Mathematics for Machine Learning, and Data Literacy are recommended modules, followed by Statistical Learning, Probabilistic Inference and Learning and Deep Learning in the next semester. At least one practical course and one seminar are recommended for the second or third semester. The master thesis is recommended for the fourth semester.

The Master’s program in machine learning is research-oriented and will enable graduates to analyze, implement, leverage, and modify techniques of machine learning. Education in problem solving capabilities is a central training objective.

Students have the opportunity to advance their knowledge and skills to a level which will allow them to get involved in top national and international research in bioinformatics and its application to the life sciences. This includes a critical discussion of research goals, contents, proposals, and research problems, and requires a high scientific level.

Is study abroad expected?

Information on the Erasmus program (Uppsala, Schweden) for students of computer and cognitive science

Optional time abroad You may choose to spend some time abroad as part of any study program. You should start planning it 12 to 18 months before your departure.  Here you will find general information and advice on studying abroad . In addition, some departments have their own subject-specific services providing information on studying abroad.

What else do I need to know?

Computer science in Tübingen is one of the leading CS departments in Germany in terms of research, and it is internationally renowned. The department collaborates with major enterprises and research institutes both nationally and internationally. The CS department places strong emphasis on interdisciplinary cooperation with other university departments, in particular medicine, biology, psychology, and media studies, and also with the three Max Planck Institutes located in Tübingen.

Tübingen is part of the Stuttgart metropolitan area - one of the strongest economical regions in Europe with a thriving local automotive and IT industry. Tübingen and its surrounding region (including Reutlingen, Böblingen, Sindelfingen and the Neckar-Alb) is the location of many university spin-offs, startups, medium-sized and large companies, with a high demand for computer scientists.

In addition to excellent future prospectives, this region offers numerous opportunities for student jobs.

In the winter term 2023/24 about 2000 students study at the Department of Computer Science:

Module handbook

Module handbook Machine Learning

The module handbook describes the goals and structure of a degree program. In the module handbook you will find details of the type of modules offered and their respective workloads as well as the classes, requirements and assessment. An ideal course plan is set out in the module handbook to help you plan your studies. The module handbook and the exam regulations help you to understand the framework and conditions of your program, and enable you to choose the right modules/ classes.

Exam regulations

You can find a formal description of the degree here .

The exam regulations contain the legally binding regulations for a degree program. They set out, for instance, which modules must be taken and which assessment must be completed in order to successfully complete the degree program. The exam regulations also regulate how often exams may be repeated, which deadlines apply to assessment, and how the final grade is calculated. The exam regulations and the module handbook help you to understand the framework and conditions of your program, and enable you to choose the right modules/ classes.

Help with choosing a study program

Is the program right for me, help with your decision to study in tübingen:.

To help you decide whether Tübingen is right for you, here are some contact points and information on studying in Germany:

University of Tübingen

• University of Tübingen’s international profile  
• Contact: University office for the Advising and Admission of International Students
• Information on application for international students  
• FAQs for international students  
• Addresses and contact points for international students at the University of Tübingen  

General information

• Study and conduct research in Germany (DAAD information)  
• All degree programs in Germany
• Find scholarships   

After your studies

Career prospects.

Due to very fast technological developments in handling large amounts of data and apply findings in a wide variety of applications, there is an ever growing need for specialists in machine learning. Since this is a highly demanding area, a PhD degree is often required. Graduates in this international Master's program will be competent in all basic and many advanced areas of machine learning, understanding and suitably applying this increasingly essential tool for dealing with large datasets, be it in science, industry or alternative domains. The studies program deals both with generic methods and their applications to specific fields, making it highly relevant for new career and job market purposes. In this whole Master program, besides professional expertise, graduates will also acquire language skills and intercultural competence due to the program’s international nature – another requirement of the international job market, both in academia and without.

Advice and internships The University’s Career Service assists with careers orientation and getting started in your profession . Jobs and internships can be found in the Praxisportal .

Doctorate options

The master program qualifies for doctoral studies in Computer Science. For further information see the website  Faculty of Science - Doctoral Studies .

Join the alumni society

Information about the  Alumni of Tübingen .

Marjolein Bolten, succesfully defended her Master thesis. Monitoring training load and identifying fatigue in young elite speed skaters using machine learning methods Monitoring training load and identifying fatigue in young elite speed skaters using machine learning methods

Bolten, Marjolein (2024)  Monitoring training load and identifying fatigue in young elite speed skaters using machine learning methods.  University of Twente, Enschede. 

More information: Thesis

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