msc computer science thesis pdf

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Computer Science Masters Theses

Theses from 2024 2024.

Enabling smart healthcare applications through visible light communication networks , Jack Manhardt

Time series anomaly detection using generative adversarial networks , Shyam Sundar Saravanan

Theses from 2023 2023

DYNAMIC DISCOUNTED SATISFICING BASED DRIVER DECISION PREDICTION IN SEQUENTIAL TAXI REQUESTS , Sree Pooja Akula

MAT: Genetic Algorithms Based Multi-Objective Adversarial Attack on Multi-Task Deep Neural Networks , Nikola Andric

COMPUTER VISION IN ADVERSE CONDITIONS: SMALL OBJECTS, LOW-RESOLUTION IMAGES, AND EDGE DEPLOYMENT , Raja Sunkara

Theses from 2022 2022

Maximising social welfare in selfish multi-modal routing using strategic information design for quantal response travelers , Sainath Sanga

Man-in-the-Middle Attacks on MQTT based IoT networks , Henry C. Wong

Theses from 2021 2021

Biochemical assay invariant attestation for the security of cyber-physical digital microfluidic biochips , Fredrick Eugene Love II

Theses from 2020 2020

On predicting stopping time of human sequential decision-making using discounted satisficing heuristic , Mounica Devaguptapu

Theses from 2019 2019

Advanced techniques for improving canonical genetic programming , Adam Tyler Harter

Evolved parameterized selection for evolutionary algorithms , Samuel Nathan Richter

Design and implementation of applications over delay tolerant networks for disaster and battlefield environment , Karthikeyan Sachidanandam

Theses from 2018 2018

Mixed-criticality real-time task scheduling with graceful degradation , Samsil Arefin

CARD: Concealed and remote discovery of IoT devices in victims' home networks , Sammie Lee Bush

Multiple security domain non deducibility in the FREEDM smart grid infrastructure , Manish Jaisinghani

Reputation and credit based incentive mechanism for data-centric message delivery in delay tolerant networks , Himanshu Jethawa

Solidification rate detection through solid-liquid interface tracking , Wei Luo

Cloud transactions and caching for improved performance in clouds and DTNs , Dileep Mardham

Cyber-physical security of an electric microgrid , Prashanth Palaniswamy

An approach for formal analysis of the security of a water treatment testbed , Sai Sidharth Patlolla

Analyzing large scale trajectory data to identify users with similar behavior , Tyler Clark Percy

Precise energy efficient scheduling of mixed-criticality tasks & sustainable mixed-criticality scheduling , Sai Sruti

A network tomography approach for traffic monitoring in smart cities , Ruoxi Zhang

Improved CRPD analysis and a secure scheduler against information leakage in real-time systems , Ying Zhang

Theses from 2017 2017

Cyber-physical security of a chemical plant , Prakash Rao Dunaka

UFace: Your universal password no one can see , Nicholas Steven Hilbert

Multi stage recovery from large scale failure in interdependent networks , Maria Angelin John Bosco

Multiple security domain model of a vehicle in an automated vehicle system , Uday Ganesh Kanteti

Personalizing education with algorithmic course selection , Tyler Morrow

Decodable network coding in wireless network , Junwei Su

Multiple security domain nondeducibility air traffic surveillance systems , Anusha Thudimilla

Theses from 2016 2016

Automated design of boolean satisfiability solvers employing evolutionary computation , Alex Raymond Bertels

Care-Chair: Opportunistic health assessment with smart sensing on chair backrest , Rakesh Kumar

Theses from 2015 2015

Dependability analysis and recovery support for smart grids , Isam Abdulmunem Alobaidi

Sensor authentication in collaborating sensor networks , Jake Uriah Bielefeldt

Argumentation based collaborative software architecture design and intelligent analysis of software architecture rationale , NagaPrashanth Chanda

A Gaussian mixture model for automated vesicle fusion detection and classification , Haohan Li

Hyper-heuristics for the automated design of black-box search algorithms , Matthew Allen Martin

Aerial vehicle trajectory design for spatio-temporal task satisfaction and aggregation based on utility metric , Amarender Reddy Mekala

Design and implementation of a broker for cloud additive manufacturing services , Venkata Prashant Modekurthy

Cyber security research frameworks for coevolutionary network defense , George Daniel Rush

Energy disaggregation in NIALM using hidden Markov models , Anusha Sankara

Theses from 2014 2014

Crime pattern detection using online social media , Raja Ashok Bolla

Energy efficient scheduling and allocation of tasks in sensor cloud , Rashmi Dalvi

A cloud brokerage architecture for efficient cloud service selection , Venkata Nagarjuna Dondapati

Access control delegation in the clouds , Pavani Gorantla

Evolving decision trees for the categorization of software , Jasenko Hosic

M-Grid : A distributed framework for multidimensional indexing and querying of location based big data , Shashank Kumar

Privacy preservation using spherical chord , Doyal Tapan Mukherjee

Top-K with diversity-M data retrieval in wireless sensor networks , Kiran Kumar Puram

On temporal and frequency responses of smartphone accelerometers for explosives detection , Srinivas Chakravarthi Thandu

Efficient data access in mobile cloud computing , Siva Naga Venkata Chaitanya Vemulapalli

An empirical study on symptoms of heavier internet usage among young adults , SaiPreethi Vishwanathan

Theses from 2013 2013

Sybil detection in vehicular networks , Muhammad Ibrahim Almutaz

Argumentation placement recommendation and relevancy assessment in an intelligent argumentation system , Nian Liu

Security analysis of a cyber physical system : a car example , Jason Madden

Efficient integrity verification of replicated data in cloud , Raghul Mukundan

Search-based model summarization , Lokesh Krishna Ravichandran

Hybridizing and applying computational intelligence techniques , Jeffery Scott Shelburg

Secure design defects detection and correction , Wenquan Wang

Theses from 2012 2012

Robust evolutionary algorithms , Brian Wesley Goldman

Semantic preserving text tepresentation and its applications in text clustering , Michael Howard

Vehicle path verification using wireless sensor networks , Gerry W. Howser

Distributed and collaborative watermarking in relational data , Prakash Kumar

Theses from 2011 2011

A social network of service providers for trust and identity management in the Cloud , Makarand Bhonsle

Adaptive rule-based malware detection employing learning classifier systems , Jonathan Joseph Blount

A low-cost motion tracking system for virtual reality applications , Abhinav Chadda

Optimization of textual affect entity relation models , Ajith Cherukad Jose

MELOC - memory and location optimized caching for mobile Ad hoc networks , Lekshmi Manian Chidambaram

A framework for transparent depression classification in college settings via mining internet usage patterns , Raghavendra Kotikalapudi

An incentive based approach to detect selfish nodes in Mobile P2P network , Hemanth Meka

Location privacy policy management system , Arej Awodha Muhammed

Exploring join caching in programming codes to reduce runtime execution , Swetha Surapaneni

Theses from 2010 2010

Event detection from click-through data via query clustering , Prabhu Kumar Angajala

Population control in evolutionary algorithms , Jason Edward Cook

Dynamic ant colony optimization for globally optimizing consumer preferences , Pavitra Dhruvanarayana

EtherAnnotate: a transparent malware analysis tool for integrating dynamic and static examination , Joshua Michael Eads

Representation and validation of domain and range restrictions in a relational database driven ontology maintenance system , Patrick Garrett. Edgett

Cloud security requirements analysis and security policy development using a high-order object-oriented modeling technique , Kenneth Kofi Fletcher

Multi axis slicing for rapid prototyping , Divya Kanakanala

Content based image retrieval for bio-medical images , Vikas Nahar

2-D path planning for direct laser deposition process , Swathi Routhu

Contribution-based priority assessment in a web-based intelligent argumentation network for collaborative software development , Maithili Satyavolu

An artificial life approach to evolutionary computation: from mobile cellular algorithms to artificial ecosystems , Shivakar Vulli

Intelligent computational argumentation for evaluating performance scores in multi-criteria decision making , Rubal Wanchoo

Minimize end-to-end delay through cross-layer optimization in multi-hop wireless sensor networks , Yibo Xu

Theses from 2009 2009

Information flow properties for cyber-physical systems , Rav Akella

Exploring the use of a commercial game engine for the development of educational software , Hussain Alafaireet

Automated offspring sizing in evolutionary algorithms , André Chidi Nwamba

Theses from 2008 2008

Image analysis techniques for vertebra anomaly detection in X-ray images , Mohammed Das

Cross-layer design through joint routing and link allocation in wireless sensor networks , Xuan Gong

A time series classifier , Christopher Mark Gore

An economic incentive based routing protocol incorporating quality of service for mobile peer-to-peer networks , Anil Jade

Incorporation of evidences in an intelligent argumentation network for collaborative engineering design , Ekta Khudkhudia

PrESerD - Privacy ensured service discovery in mobile peer-to-peer environment , Santhosh Muthyapu

Co-optimization: a generalization of coevolution , Travis Service

Critical infrastructure protection and the Domain Name Service (DNS) system , Mark Edward Snyder

Co-evolutionary automated software correction: a proof of concept , Joshua Lee Wilkerson

Theses from 2007 2007

A light-weight middleware framework for fault-tolerant and secure distributed applications , Ian Jacob Baird

Symbolic time series analysis using hidden Markov models , Nikhil Bhardwaj

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Home > CICS > CS > CS_DISS

Computer Science Department Dissertations Collection

Dissertations from 2024 2024.

Enabling Privacy and Trust in Edge AI Systems , Akanksha Atrey, Computer Science

Generative Language Models for Personalized Information Understanding , Pengshan Cai, Computer Science

Towards Automatic and Robust Variational Inference , Tomas Geffner, Computer Science

Multi-SLAM Systems for Fault-Tolerant Simultaneous Localization and Mapping , Samer Nashed, Computer Science

Policy Gradient Methods: Analysis, Misconceptions, and Improvements , Christopher P. Nota, Computer Science

Data to science with AI and human-in-the-loop , Gustavo Perez Sarabia, Computer Science

Question Answering By Case-Based Reasoning With Textual Evidence , Dung N. Thai, Computer Science

Dissertations from 2023 2023

An Introspective Approach for Competence-Aware Autonomy , Connor Basich, Computer Science

Foundations of Node Representation Learning , Sudhanshu Chanpuriya, Computer Science

Learning to See with Minimal Human Supervision , Zezhou Cheng, Computer Science

IMPROVING USER EXPERIENCE BY OPTIMIZING CLOUD SERVICES , Ishita Dasgupta, Computer Science

Automating the Formal Verification of Software , Emily First, Computer Science

Learning from Sequential User Data: Models and Sample-efficient Algorithms , Aritra Ghosh, Computer Science

Human-Centered Technologies for Inclusive Collection and Analysis of Public-Generated Data , Mahmood Jasim, Computer Science

Rigorous Experimentation For Reinforcement Learning , Scott M. Jordan, Computer Science

Towards Robust Long-form Text Generation Systems , Kalpesh Krishna, Computer Science

Emerging Trustworthiness Issues in Distributed Learning Systems , Hamid Mozaffari, Computer Science

TOWARDS RELIABLE CIRCUMVENTION OF INTERNET CENSORSHIP , Milad nasresfahani, Computer Science

Evidence Assisted Learning for Clinical Decision Support Systems , Bhanu Pratap Singh Rawat, Computer Science

DESIGN AND ANALYSIS OF CONTENT CACHING SYSTEMS , Anirudh Sabnis, Computer Science

Quantifying and Enhancing the Security of Federated Learning , Virat Vishnu Shejwalkar, Computer Science

Effective and Efficient Transfer Learning in the Era of Large Language Models , Tu Vu, Computer Science

Data-driven Modeling and Analytics for Greening the Energy Ecosystem , John Wamburu, Computer Science

Bayesian Structural Causal Inference with Probabilistic Programming , Sam A. Witty, Computer Science

LEARNING TO RIG CHARACTERS , Zhan Xu, Computer Science

GRAPH REPRESENTATION LEARNING WITH BOX EMBEDDINGS , Dongxu Zhang, Computer Science

Dissertations from 2022 2022

COMBINATORIAL ALGORITHMS FOR GRAPH DISCOVERY AND EXPERIMENTAL DESIGN , Raghavendra K. Addanki, Computer Science

MEASURING NETWORK INTERFERENCE AND MITIGATING IT WITH DNS ENCRYPTION , Seyed Arian Akhavan Niaki, Computer Science

Few-Shot Natural Language Processing by Meta-Learning Without Labeled Data , Trapit Bansal, Computer Science

Communicative Information Visualizations: How to make data more understandable by the general public , Alyxander Burns, Computer Science

REINFORCEMENT LEARNING FOR NON-STATIONARY PROBLEMS , Yash Chandak, Computer Science

Modeling the Multi-mode Distribution in Self-Supervised Language Models , Haw-Shiuan Chang, Computer Science

Nonparametric Contextual Reasoning for Question Answering over Large Knowledge Bases , Rajarshi Das, Computer Science

Languages and Compilers for Writing Efficient High-Performance Computing Applications , Abhinav Jangda, Computer Science

Controllable Neural Synthesis for Natural Images and Vector Art , Difan Liu, Computer Science

Probabilistic Commonsense Knowledge , Xiang Li, Computer Science

DISTRIBUTED LEARNING ALGORITHMS: COMMUNICATION EFFICIENCY AND ERROR RESILIENCE , Raj Kumar Maity, Computer Science

Practical Methods for High-Dimensional Data Publication with Differential Privacy , Ryan H. McKenna, Computer Science

Incremental Non-Greedy Clustering at Scale , Nicholas Monath, Computer Science

High-Quality Automatic Program Repair , Manish Motwani, Computer Science

Unobtrusive Assessment of Upper-Limb Motor Impairment Using Wearable Inertial Sensors , Brandon R. Oubre, Computer Science

Mixture Models in Machine Learning , Soumyabrata Pal, Computer Science

Decision Making with Limited Data , Kieu My Phan, Computer Science

Neural Approaches for Language-Agnostic Search and Recommendation , Hamed Rezanejad Asl Bonab, Computer Science

Low Resource Language Understanding in Voice Assistants , Subendhu Rongali, Computer Science

Enabling Daily Tracking of Individual’s Cognitive State With Eyewear , Soha Rostaminia, Computer Science

LABELED MODULES IN PROGRAMS THAT EVOLVE , Anil K. Saini, Computer Science

Reliable Decision-Making with Imprecise Models , Sandhya Saisubramanian, Computer Science

Data Scarcity in Event Analysis and Abusive Language Detection , Sheikh Muhammad Sarwar, Computer Science

Representation Learning for Shape Decomposition, By Shape Decomposition , Gopal Sharma, Computer Science

Metareasoning for Planning and Execution in Autonomous Systems , Justin Svegliato, Computer Science

Approximate Bayesian Deep Learning for Resource-Constrained Environments , Meet Prakash Vadera, Computer Science

ANSWER SIMILARITY GROUPING AND DIVERSIFICATION IN QUESTION ANSWERING SYSTEMS , Lakshmi Nair Vikraman, Computer Science

Dissertations from 2021 2021

Neural Approaches to Feedback in Information Retrieval , Keping Bi, Computer Science

Sociolinguistically Driven Approaches for Just Natural Language Processing , Su Lin Blodgett, Computer Science

Enabling Declarative and Scalable Prescriptive Analytics in Relational Data , Matteo Brucato, Computer Science

Neural Methods for Answer Passage Retrieval over Sparse Collections , Daniel Cohen, Computer Science

Utilizing Graph Structure for Machine Learning , Stefan Dernbach, Computer Science

Enhancing Usability and Explainability of Data Systems , Anna Fariha, Computer Science

Algorithms to Exploit Data Sparsity , Larkin H. Flodin, Computer Science

3D Shape Understanding and Generation , Matheus Gadelha, Computer Science

Robust Algorithms for Clustering with Applications to Data Integration , Sainyam Galhotra, Computer Science

Improving Evaluation Methods for Causal Modeling , Amanda Gentzel, Computer Science

SAFE AND PRACTICAL MACHINE LEARNING , Stephen J. Giguere, Computer Science

COMPACT REPRESENTATIONS OF UNCERTAINTY IN CLUSTERING , Craig Stuart Greenberg, Computer Science

Natural Language Processing for Lexical Corpus Analysis , Abram Kaufman Handler, Computer Science

Social Measurement and Causal Inference with Text , Katherine A. Keith, Computer Science

Concentration Inequalities in the Wild: Case Studies in Blockchain & Reinforcement Learning , A. Pinar Ozisik, Computer Science

Resource Allocation in Distributed Service Networks , Nitish Kumar Panigrahy, Computer Science

History Modeling for Conversational Information Retrieval , Chen Qu, Computer Science

Design and Implementation of Algorithms for Traffic Classification , Fatemeh Rezaei, Computer Science

SCALING DOWN THE ENERGY COST OF CONNECTING EVERYDAY OBJECTS TO THE INTERNET , Mohammad Rostami, Computer Science

Deep Learning Models for Irregularly Sampled and Incomplete Time Series , Satya Narayan Shukla, Computer Science

Traffic engineering in planet-scale cloud networks , Rachee Singh, Computer Science

Video Adaptation for High-Quality Content Delivery , Kevin Spiteri, Computer Science

Learning from Limited Labeled Data for Visual Recognition , Jong-Chyi Su, Computer Science

Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications , Amee Trivedi, Computer Science

Geometric Representation Learning , Luke Vilnis, Computer Science

Understanding of Visual Domains via the Lens of Natural Language , Chenyun Wu, Computer Science

Towards Practical Differentially Private Mechanism Design and Deployment , Dan Zhang, Computer Science

Audio-driven Character Animation , Yang Zhou, Computer Science

Dissertations from 2020 2020

Noise-Aware Inference for Differential Privacy , Garrett Bernstein, Computer Science

Motion Segmentation - Segmentation of Independently Moving Objects in Video , Pia Katalin Bideau, Computer Science

An Empirical Assessment of the Effectiveness of Deception for Cyber Defense , Kimberly J. Ferguson-Walter, Computer Science

Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems , Richard Freedman, Computer Science

Improving Reinforcement Learning Techniques by Leveraging Prior Experience , Francisco M. Garcia, Computer Science

Optimization and Training of Generational Garbage Collectors , Nicholas Jacek, Computer Science

Understanding the Dynamic Visual World: From Motion to Semantics , Huaizu Jiang, Computer Science

Improving Face Clustering in Videos , SouYoung Jin, Computer Science

Reasoning About User Feedback Under Identity Uncertainty in Knowledge Base Construction , Ariel Kobren, Computer Science

Learning Latent Characteristics of Data and Models using Item Response Theory , John P. Lalor, Computer Science

Higher-Order Representations for Visual Recognition , Tsung-Yu Lin, Computer Science

Learning from Irregularly-Sampled Time Series , Steven Cheng-Xian Li, Computer Science

Dynamic Composition of Functions for Modular Learning , Clemens GB Rosenbaum, Computer Science

Improving Visual Recognition With Unlabeled Data , Aruni Roy Chowdhury, Computer Science

Deep Neural Networks for 3D Processing and High-Dimensional Filtering , Hang Su, Computer Science

Towards Optimized Traffic Provisioning and Adaptive Cache Management for Content Delivery , Aditya Sundarrajan, Computer Science

The Limits of Location Privacy in Mobile Devices , Keen Yuun Sung, Computer Science

ALGORITHMS FOR MASSIVE, EXPENSIVE, OR OTHERWISE INCONVENIENT GRAPHS , David Tench, Computer Science

System Design for Digital Experimentation and Explanation Generation , Emma Tosch, Computer Science

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You are here Publications > M.Sc. Theses

M.Sc. Theses

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Thesis Examples

Latex Example (shortened M.Sc. with urthesis.sty)  (ZIP)

Latex Example (complete M.Sc. with no .sty)  (ZIP)

How to Write a M.Sc. Thesis

The following guide to writing an M.Sc. thesis was prepared by Howard Hamilton and Brien Maguire, based on previous guides by Alan Mackworth (University of British Columbia) and Nick Cercone (Simon Fraser University), with their permission.

Quick Guide to the M.Sc. Thesis

An acceptable M.Sc. thesis in Computer Science should attempt to satisfy one or more of the following criteria:

• Original research results are explained clearly and concisely.
• The thesis explains a novel exploratory implementation or a novel empirical study whose results will be of interest to the Computer Science community in general and to a portion of the Computer Science community in particular, e.g., Artificial Intelligence, Computational Complexity, etc.
• Novel implementation techniques are outlined, generalized, and explained.
• Theoretical results are obtained, explained, proven, and (worst, best, average) case analysis is performed where applicable.
• The implementation of a practical piece of nontrivial software whose availability could have some impact on the Computer Science community. Examples are a distributed file system for a mobile computing environment and a program featuring the application of artificial intelligence knowledge representation and planning techniques to intelligent computer assisted learning software.

Writing an acceptable thesis can be a painful and arduous task, especially if you have not written much before. A good methodology to follow, immediately upon completion of the required courses, is to keep a paper or electronic research notebook and commit to writing research oriented notes in it every day. From time to time, organize or reorganize your notes under headings that capture important categories of your thoughts. This journal of your research activities can serve as a very rough draft of your thesis by the time you complete your research. From these notes to a first M.Sc. thesis draft is a much less painful experience than to start a draft from scratch many months after your initial investigations. To help structure an M.Sc. thesis, the following guide may help.

One Formula for an M.Sc. Thesis for Computer Science

Chapter 1 Introduction: This chapter contains a discussion of the general area of research which you plan to explore in the thesis. It should contain a summary of the work you propose to carry out and the motivations you can cite for performing this work. Describe the general problem that you are working towards solving and the specific problem that you attempt to solve in the thesis. For example, the general problem may be finding an algorithm to help an artificial agent discover a path in a novel environment, and the specific problem may be evaluating the relative effectiveness and efficiency of five particular named approaches to finding the shortest path in a graph where each node is connected to at most four neighbours, with no knowledge of the graph except that obtained by exploration. This chapter should also explain the motivations for solving each of the general problem and your specific problem. The chapter should end with a guide to the reader on the composition and contents of the rest of the thesis, chapter by chapter. If there are various paths through the thesis, these should also be explained in Chapter 1.

Chapter 2 Limited Overview of the Field: This chapter contains a specialized overview of that part of a particular field in which you are doing M.Sc. thesis research, for example, paramodulation techniques for automated theorem proving or bubble figure modelling strategies for animation systems. The survey should not be an exhaustive survey but rather should impose some structure on your field of research endeavour and carve out your niche within the structure you impose. You should make generous use of illustrative examples and citations to current research.

Chapter 3 My Theory/Solution/Algorithm/Program: This chapter outlines your proposed solution to the specific problem described in Chapter 1. The solution may be an extension to, an improvement of, or even a disproof of someone else's theory / solution / method / ...).

Chapter 4 Description of Implementation or Formalism: This chapter describes your implementation or formalism. Depending on its length, it may be combined with Chapter 3. Not every thesis requires an implementation. Prototypical implementations are common and quite often acceptable although the guiding criterion is that the research problem must be clearer when you've completed your task than it was when you started!

Chapter 5 Results and Evaluation: This chapter should present the results of your thesis. You should choose criteria by which to judge your results, for example, the adequacy, coverage, efficiency, productiveness, effectiveness, elegance, user friendliness, etc., and then clearly, honestly and fairly adjudicate your results according to fair measures and report those results. You should repeat, whenever possible, these tests against competing or previous approaches (if you are clever you will win hands down in such comparisons or such comparisons will be obviated by system differences). The competing or previous approaches you compare against must have been introduced in Chapter 2 (in fact that may be the only reason they actively appear in Chapter 2) and you should include pointers back to Chapter 2. Be honest in your evaluations. If you give other approaches the benefit of the doubt every time, and develop a superior technique, your results will be all the more impressive.

Chapter 6 Conclusions: This chapter should summarize the achievements of your thesis and discuss their impact on the research questions you raised in Chapter 1. Use the distinctive phrasing "An original contribution of this thesis is" to identify your original contributions to research. If you solved the specific problem described in Chapter 1, you should explicitly say so here. If you did not, you should also make this clear. You should indicate open issues and directions for further or future work in this area with your estimates of relevance to the field, importance and amount of work required.

References Complete references for all cited works. This should not be a bibliography of everything you have read in your area.

Appendices include technical material (program listings, output, graphical plots of data, detailed tables of experimental results, detailed proofs, etc.) which would disrupt the flow of the thesis but should be made available to help explain or provide details to the curious reader.

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Master's Project Reports

  Chen, Catherine

  Chen, Yiwen and Ren, Jiahao

  Chernosky, Brynn

  Christou, Neophytes

  Dekle, Max

  Demetci, Pinar

  Fu, Changcheng

  Fu, Haotian

  Goktas, Denizalp

  Golovanevsky, Michal

  Howe, Wyatt

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  Luo, Calvin

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  Peng, Kathy

  Ramesh, Dev

  Ryjikov, Benjamin

  Scherick, James

  Sriram, Abhinav

  Zhou, Peisen

  Zhou, Tongyu

  Zhuo, Wang

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You are here Publications > M.Sc. Dissertations

Dissertations

Department of Computer Science

Cs913 dissertation project, cs913-60 dissertation project in data analytics.

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Introductory description

The dissertation is intended to give students the opportunity to consolidate the knowledge that they have acquired during the first half of the MSc, and to undertake a research led project. Students are expected to carry out a significant development exercise, either in the form of a research project or a knowledge transfer project that is applying recent research and the advanced topics taught in the first half of the course.

Module aims

The aim of your dissertation is to give you the opportunity to consolidate the knowledge that you have gained during the taught component of your MSc through a research-led project. You are expected to carry out a significant development exercise, either in the form of a research project or a knowledge transfer project that applies the topics taught in your course. The project will require strong project management skills, problem-solving abilities, and self directed study. Although not a requirement, there is scope for industrial involvement in dissertation projects, and this is encouraged. The dissertation also provides opportunity for interdisciplinary work, again building on the the modules taught earlier in the course, and will require students to demonstrate a mature knowledge of computer science and its applications.

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The research interests of staff members, as typically represented (but not restricted to) the modules taught in the first six months of the MSc, will be the major source of dissertation topics. A degree of industrial input and involvement will be encouraged, and can be facilitated through existing academic-industrial collaborations or by addressing specific topics that are of interest to industrial partners. The dissertation project will be prefaced by introductory workshops on issues of project management and planning. All projects will be closely supervised by academics with ongoing feedback and guidance at all stages of the project from the conception to completion.

Learning outcomes

By the end of the module, students should be able to:

• Carry out a comprehensive research project and critically interpret results in computer science and applications.
• Demonstrate a detailed knowledge and understanding of one area of computer science at, or approaching, the frontiers of research.
• Interpret and evaluate results in computer science.
• Demonstrate independent learning skills.
• Write an extended scientific report and show research skills (including the use of library and web resources).
• Show good oral communication skills.

Research element

Research paper reading. Research literature analysis and critique.

Subject specific skills

Computer science research skills.

Transferable skills

Technical - Experience in undertaking critical reading and interpretation of technical articles. An understanding of the hardware and software systems that are linked to the area of the dissertation. Technical skills in the analysis, design and implementation of complex systems in support of a research and /or commercial goal. Communication - Lecture listening. Technical report writing. Technical document comprehension and analysis. Documenting software solutions. Research paper reading. Presentation skills. Critical Thinking - Systems analysis and technical problem solving. Research literature analysis and critique. Multitasking - Management of competing deadlines and priorities. Management of parallel project activities. Teamwork - Working under the supervision of a an academic advisor. Creativity - Developing solutions to a research or industrial problem. Leadership - Combining critical thinking and technical understanding in the development of an original solution, whilst being able to convey the process to an informed audience and being receptive to supervisory support.

Private study description

Reading, programming, system analysis, system design, system implementation, supporting meetings, project management, presenting and document writing.

No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Assessment group A2

Assessment group r, feedback on assessment.

Written feedback from supervisor (progress report, presentation and dissertation) and second marker (presentation and dissertation) with additional oral feedback from supervisor.

This module is Core for:

• Year 1 of TCSA-G5PA Postgraduate Taught Data Analytics

Further Information

Terms 2, 3 and Summer

Online Material

Previous MSc Theses

"Creating and Testing a Web-based User Interface for the Advanced Symptom Management System (ASyMS)." F. N. L. Henok. G. Wilson. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) IM

"Synthesising Images by Imagination." T. Y. Chen. D. Roussinov. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ASE

"Detecting Coastal Litter with Neural Networks." L. Smith. M. Roper. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ACSBD

"Using Recurrent Neural Networks to Generate Music." L. Devlin. D. Roussinov. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"A Patch-Based Deep Learning Approach for Land-Classification of Sentinel-2 Satellite Imagery." D. Smith. M. Roper. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ACSBD

"Predicting the Resolution Time and Priority of Bug Reports: A Deep Learning Approach." M. Mihaylov. M. Roper. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ACS

"Reducing Desk-Based Sedentary Behaviour in University Settings using Personalised Digital Health Solutions: An Application of the Integrate, Design, Assess and Share (IDEAS) Framework for UX Design." C. Wani. M. Lennon. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) DHS

"Monopoly with Bitcoin." M. Tiggerdine. R. English. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ACS

"Security Data Governance System." S. Magyar. s. Terzis. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) IM

"Developing Recommendation Systems for Movies Using Graph Database Clustering." D. Moorhead. C. Kupke. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ACSBD

"A Grenetic Alogrithm for Query Optimization." A. Russell. Y. Moshfeghi. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ACSBD

"Topic Modelling, Sentiment Analsys and Classification of Short-Form Text Customer Journey of Insurance Purchases." L. Stoyanova. W. Wallace. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) IMIP

"The Prediction of Student Performance Through the Use of Machine Learning." A. Bruce. K. Liaskos. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"Extracting Data from Insurance Documents with Natural Language Processing and Machine Learning." J. MacKenzie. W. Wallace. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"Cyberbullying and Character." R. Fowlds. D. McMenemy. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"Development of Chinese Libraries in 20th Century Singapore." O. Z. Jia. I. Ruthven. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"Automated personalised music reproducer according to running speed by means of Machine Learning." A. Giordano. K. Liaskos. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"Automated Code Quality Metrics for Concurrent Software." N. Mirshafiee. K. Liaskos. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"Walk Scotland: Technology on the Trail - Developing a Comprehensive Mobile Travel Management Application for Scotland's Hiking Trails." J. Swope. K. Egan. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"Bias within Knowledge Organisation Systems: From Critical Analysis to Critical Praxis." H. Hanratty. D. McMenemy. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"Automated Mutation Testing for Concurrent Software." P. Gray. C. Revie. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"Analysing the use of casting in Java systems." P. O'Hear. M. Wood. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"Evidence Based Library and Information Practice (EBLIP): A Comparative Study of UK and US Academic Librarians." M. Black. D. McMenemy. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"The Organisational Impact of an Archive of News Subtitles: Usefulness and Accessibility." C. Hicks. M. Halvey. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"Machine Learning Algorithms for Sports' Results Prediction." A. Igea. K. Liaskos. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"An exploration of Cryptanalysis Learning Software through the Zodiac Cryptograms." R. J. McRae. R. English. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"Digital Collaboration and Educational Resources: National Library of Scotland and Education Sector Case Study." A. Bowie. S. Buchanan. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"Authority, Credibility and Trust in Vegan Blogs: Methods used by Content Creators in the Presentation of Information." L. Machnee. I. Ruthven. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"Real time Emotion Detection." P. Konstantinou. M. Goodfellow. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"An Application for Indicating Cognitive Competence as measured by the Mini Mental State Exam." S. McQuoney. W. Wallace. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"Library Anxiety in the Health Library Context." E. Carney. I. Ruthven. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"Networked Intimacies: Social Media Policies and Regulation on Adult Content." W. Michaelsen. D. McMenemy. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"Development of a Scalable Online Booking System for SME Car Rental Systems through implementation of the MERN Stack." J. Preston. M. Roper. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"Front-end development of a modern CRM web application using React." A. W. Ross. K. Liaskos. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) SD

"Informed Consent and Privacy in Scotland's Public Libraries." S. Connor. D. McMenemy. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"A Case Study of the David Fanshawe World Music Archive." K. Morgan. D. Pennington. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"Has FRBR Revolutionised Our Catalogues? A Comparative Analysis of AACR2 and RDA-Formatted Records to the FRBR Model." A. Stein. D. Pennington. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"Makerspaces in Public Libraries in Scotland: A Study of Progress and Best Practice." C. Rae. D. Pennington. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"An Evaluation of Scottish Higher Education Institutional Repositories." K. Veitch. G. Macgregor. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

"Bibliotherapy in the Public Library: An Analysis of the Concept and Recommendations for Practice." M. Wideman. I. Ruthven. Department of Computer and Information Sciences, University of Strathclyde. 2019. Abstract Download PDF ( BibTeX ) ILS

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Master of Science (M.Sc.) Computer Science (Thesis) (45 credits)

Thesis courses (29 credits).

29 credits selected from:

Offered by: Computer Science ( Faculty of Science )

Administered by: Graduate Studies

Computer Science (Sci) : Ongoing research pertaining to thesis.

Terms: This course is not scheduled for the 2024-2025 academic year.

Instructors: There are no professors associated with this course for the 2024-2025 academic year.

Restriction: Computer Science students

Required Courses (2 credits)

Computer Science (Sci) : Exposure to ongoing research directions in computer science through regular attendance of the research colloquium organized by the School of Computer Science.

Complementary Courses (14 credits)

14 credits of COMP (or approved) courses at the 500-, 600-, or 700-level.

Complementary courses must satisfy a Computer Science breadth requirement, with at least one course in two of the Theory, Systems, and Application areas. Areas covered by specific courses are determined by the Computer Science graduate program director.

Category A: Theory

Computer Science (Sci) : State-of-the-art language-based techniques for enforcing security policies in distributed computing environments. Static techniques (such as type- and proof-checking technology), verification of security policies and applications such as proof-carrying code, certifying compilers, and proof-carrying authentication.

Prerequisites: COMP 302 , COMP 330 .

Computer Science (Sci) : Propositional logic - syntax and semantics, temporal logic, other modal logics, model checking, symbolic model checking, binary decision diagrams, other approaches to formal verification.

Prerequisites: COMP 251 and COMP 330 .

Computer Science (Sci) : Introduction to modern constructive logic, its mathematical properties, and its numerous applications in computer science.

Prerequisite: COMP 302

Restriction: Not open to students who have taken COMP 426

Computer Science (Sci) : Models for sequential and parallel computations: Turing machines, boolean circuits. The equivalence of various models and the Church-Turing thesis. Unsolvable problems. Model dependent measures of computational complexity. Abstract complexity theory. Exponentially and super-exponentially difficult problems. Complete problems.

Prerequisite: COMP 330

Computer Science (Sci) : Designing and programming reliable numerical algorithms. Stability of algorithms and condition of problems. Reliable and efficient algorithms for solution of equations, linear least squares problems, the singular value decomposition, the eigenproblem and related problems. Perturbation analysis of problems. Algorithms for structured matrices.

Prerequisite: MATH 327 or COMP 350

Computer Science (Sci) : This course presents an in-depth study of modern cryptography and data security. The basic information theoretic and computational properties of classical and modern cryptographic systems are presented, followed by a cryptanalytic examination of several important systems. We will study the applications of cryptography to the security of systems.

Prerequisites: COMP 360 or COMP 362 , MATH 323 .

Computer Science (Sci) : Algorithmic and structural approaches in combinatorial optimization with a focus upon theory and applications. Topics include: polyhedral methods, network optimization, the ellipsoid method, graph algorithms, matroid theory and submodular functions.

Prerequisite: Math 350 or COMP 362 (or equivalent).

Restriction: This course is reserved for undergraduate honours students and graduate students. Not open to students who have taken or are taking MATH 552 .

Computer Science (Sci) : Foundations of game theory. Computation aspects of equilibria. Theory of auctions and modern auction design. General equilibrium theory and welfare economics. Algorithmic mechanism design. Dynamic games.

Prerequisite: COMP 362 or MATH 350 or MATH 454 or MATH 487 , or instructor permission

Restriction: Not open to students who are taking or have taken MATH 553

Computer Science (Sci) : The theory and application of approximation algorithms. Topics include: randomized algorithms, network optimization, linear programming and semi definite programming techniques, the game theoretic method, the primal-dual method, and metric embeddings.

Prerequisites: COMP 362 or MATH 350 or permission of instructor. Strong background in algorithms and/or mathematics.

Restriction: Not open to students who have taken COMP 692

Computer Science (Sci) : Concentration inequalities, PAC model, VC dimension, Rademacher complexity, convex optimization, gradient descent, boosting, kernels, support vector machines, regression and learning bounds. Further topics selected from: Gaussian processes, online learning, regret bounds, basic neural network theory.

Prerequisites: MATH 462 or COMP 451 or ( COMP 551 , MATH 222 , MATH 223 and MATH 324 ) or ECSE 551 .

Restrictions: Not open to students who have taken or are taking MATH 562 . Not open to students who have taken COMP 599 when the topic was "Statistical Learning Theory" or "Mathematical Topics for Machine Learning". Not open to students who have taken COMP 598 when the topic was "Mathematical Foundations of Machine Learning".

Computer Science (Sci) : Use of computer in solving problems in discrete optimization. Linear programming and extensions. Network simplex method. Applications of linear programming. Vertex enumeration. Geometry of linear programming. Implementation issues and robustness. Students will do a project on an application of their choice.

Prerequisites: COMP 360 and MATH 223

Computer Science (Sci) : Formulation, solution and applications of integer programs. Branch and bound, cutting plane, and column generation algorithms. Combinatorial optimization. Polyhedral methods. A large emphasis will be placed on modelling. Students will select and present a case study of an application of integer programming in an area of their choice.

Prerequisites: COMP 566 or MATH 417

Computer Science (Sci) : Study of elementary data structures: lists, stacks, queues, trees, hash tables, binary search trees, red-black trees, heaps. Augmenting data structures. Sorting and selection, Recursive algorithms. Advanced data structures including binomial heaps, Fibonacci heaps, disjoint set structures, and splay trees. Amortizing. String algorithms. Huffman trees and suffix trees. Graph algorithms.

Computer Science (Sci) : Introduction to mathematical concepts important across computer science, how to think mathematically, and how to write proofs. Proof techniques such as induction, contradiction, and monovariants; topics in combinatorics, graph theory, algebra, analysis, and probability; mathematical analysis of algorithms, data structures, and computational complexity. Emphasis on the mathematical explanations for useful concepts.

Restrictions: Not open to students who have majored in Mathematics or an equivalent subject, or have taken a proof-based math or computer science course within the previous two years.

Not open to students who have taken COMP 761 when the topic was "Mathematical Tools for Computer Science".

Computer Science (Sci) : Efficient and reliable numerical algorithms in estimation and their applications. Linear models and least squares estimation. Maximum-likelihood estimation. Kalman filtering. Adaptive estimation, GPS measurements and mathematical models for positioning. Position estimation. Fault detection and exclusion.

Prerequisites: MATH 323 , MATH 324 and COMP 350

Computer Science (Sci) : Information theoretic definitions of security, zero-knowledge protocols, secure function evaluation protocols, cryptographic primitives, privacy amplification, error correction, quantum cryptography, quantum cryptanalysis.

Prerequisite: COMP 547

Computer Science (Sci) : Review of the basic notions of cryptography and quantum information theory. Quantum key distribution and its proof of security. Quantum encryption, error-correcting codes and authentication. Quantum bit commitment, zero-knowledge and oblivious transfer. Multiparty quantum computations.

Prerequisite: COMP 547 and permission of the instructor.

Restriction: An introduction to notions of Information Theory is required.

Computer Science (Sci) : Probabilistic analysis of algorithms and data structures under random input. Expected behaviour of search trees, tries, heaps, bucket structures and multidimensional data structures. Random sampling, divide-and-conquer, grid methods. Applications in computational geometry and in game tree searching. Combinatorial search problems. Algorithms on random graphs.

Computer Science (Sci) : Advanced topics in theory related to computer science.

Category B: Systems

Computer Science (Sci) : Models and Architectures. Application-oriented communication paradigms (e.g. remote method invocation, group communication). Naming services. Synchronization (e.g. mutual exclusion, concurrency control). Fault-tolerance (e.g. process and replication, agreement protocols). Distributed file systems. Security. Examples of distributed systems (e.g. Web, CORBA). Advanced Topics.

Prerequisites: COMP 310 , COMP 251 or equivalent.

Computer Science (Sci) : The structure of a compiler. Lexical analysis. Parsing techniques. Syntax directed translation. Run-time implementation of various programming language constructs. Introduction to code generation for an idealized machine. Students will implement parts of a compiler.

3 hours, 1 hour consultation

Prerequisites: COMP 273 and COMP 302

Computer Science (Sci) : Development, analysis, and maintenance of software architectures, with special focus on modular decomposition and reverse engineering.

Prerequisite: COMP 303 .

Computer Science (Sci) : Model-driven software development; requirements engineering based on use cases and scenarios; object-oriented modelling using UML and OCL to establish complete and precise analysis and design documents; mapping to Java. Introduction to meta-modelling and model transformations, use of modelling tools.

Prerequisite: ECSE 321 or COMP 303 or COMP 361

Computer Science (Sci) : Fundamental design principles, elements, and protocols of computer networks, focusing on the current Internet. Topics include: layered architecture, direct link networks, switching and forwarding, bridge routing, congestion control, end-to-end protocols application of DNS, HTTP, P2P, fair queuing, performance modeling and analysis.

Prerequisite: COMP 310 or ECSE 427

Computer Science (Sci) : Conceptual foundations of information privacy: security and cryptography, privacy by design, privacy threats. Technical controls for supporting privacy: authorization, authentication, access control, malware and intrusion detection. Application-specific privacy concerns of databases, web and mobile applications, cloud storage.

Prerequisite: COMP 303

Restrictions: Not open to students who have taken COMP 599 when the topic was "Topics in Mobile Application Development".

Computer Science (Sci) : Architecture and examples of distributed information systems (e.g., federated databases, component systems, web databases). Data consistency (consistency models, advanced transaction models, advanced concurrency control, distributed recovery). Data replication and caching. Distribution queries, Schema Integration. Advanced Topics.

Prerequisites: COMP 421 and one of COMP 435 or COMP 535 or COMP 512 , or equivalent.

Computer Science (Sci) : Program analysis and transformations are used in optimizing compilers and other automatic tools such as bug-finders, verification tools and software engineering applications. Course topics include the design of intermediate representations, control flow analysis, data flow analysis at both the intra- and inter-procedural level and program transformations for performance improvement.

Prerequisite: COMP 251 or equivalent, COMP 302 or equivalent, COMP 520 is useful but not strictly necessary

Computer Science (Sci) : Conservative and optimistic synchronization involved in executing a discrete event simulation on a distributed platform (e.g. cluster of workstations, shared memory multiprocessor). Focus is on efficiency, strengths and limitations of the different approaches. Applications to large simulations (networks, VLSI, virtual environments).

Prerequisite: COMP 310 or equivalent.

Computer Science (Sci) : Software fault tolerance, concepts and implementation. Failure classification; information and time redundancy; forward and backward error recovery; error confinement; idealized fault-tolerant component; sequential and concurrent systems; exception handling; transactions and atomic actions; voting; design diversity. Case studies.

Prerequisite: COMP 409 or permission of instructor

Computer Science (Sci) : Advanced topics in programming.

Computer Science (Sci) : Advanced topics in computing systems.

Category C: Applications

Computer Science (Sci) : The approach and the challenges in the key components of manipulators and locomotors: representations, kinematics, dynamics, rigid-body chains, redundant systems, under-actuated systems, control, planning, and perception. Practical aspects of robotics: collisions, integrating sensory feedback, and real-time software development.

Prerequisites: MATH 223 , MATH 323 , COMP 206 , and COMP 250 , or equivalents.

Restrictions: Not open to students who have taken COMP 597 when the topic was "Applied Robotics".

Students should be comfortable with C++ (such as from COMP 322 ) and a Unix-like programming environment.

Computer Science (Sci) : Genre and history of games, basic game design, storytelling and narrative analysis, game engines, design of virtual worlds, real-time 2D graphics, game physics and physical simulation, pathfinding and game AI, content generation, 3D game concerns, multiplayer and distributed games, social issues.

Prerequisite: COMP 251 , MATH 223 and ( COMP 303 or COMP 361 ).

Computer Science (Sci) : Computational models of visual perception and audition. Vision problems include stereopsis, motion, focus, perspective, color. Audition problems include source localization and recognition. Emphasis on physics of image formation, sensory signal processing, neural pathways and computation, psychophysical methods.

Restrictions: Not open to students who have taken COMP 646 .

Computer Science (Sci) : Overview of the influence of neuroscience and psychology on Artificial Intelligence (AI). Historical topics: perceptrons, the PDP framework, Hopfield nets, Boltzmann and Helmholtz machines, and the behaviourist origins of reinforcement learning. Modern topics: deep learning, attention, memory and consciousness. Emphasis on understanding the interdisciplinary foundations of modern AI.

Prerequisites: MATH 222 , MATH 223 , and MATH 323 ; or equivalents.

Restrictions: Not open to students who have taken COMP 596 when the topic was "Brain-Inspired Artificial Intelligence".

Computer Science (Sci) : An introduction to the computational modelling of natural language, including algorithms, formalisms, and applications. Computational morphology, language modelling, syntactic parsing, lexical and compositional semantics, and discourse analysis. Selected applications such as automatic summarization, machine translation, and speech processing. Machine learning techniques for natural language processing.

Prerequisite(s): MATH 323 or ECSE 305 , COMP 251 or COMP 252

Restriction(s): Not open to students who have taken COMP 599 in 201509 or 201609.

Computer Science (Sci) : Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems.

Prerequisite(s): MATH 323 or ECSE 205 , COMP 202 , MATH 133 , MATH 222 (or their equivalents).

Restriction(s): Not open to students who have taken or are taking COMP 451 , ECSE 551 , or PSYC 560 .

Some background in Artificial Intelligence is recommended, e.g. COMP-424 or ECSE-526 , but not required.

Computer Science (Sci) : Fundamental mathematical, algorithmic and representational issues in computer graphics: overview of graphics pipeline, homogeneous coordinates, projective transformations, line-drawing and rasterization, hidden surface removal, surface modelling (quadrics, bicubics, meshes), rendering (lighting, reflectance models, ray tracing, texture mapping), compositing colour perception, and other selected topics.

Prerequisite: MATH 222 , MATH 223 , COMP 250 , COMP 206

Computer Science (Sci) : Image filtering, edge detection, image features and histograms, image segmentation, image motion and tracking, projective geometry, camera calibration, homographies, epipolar geometry and stereo, point clouds and 3D registration. Applications in computer graphics and robotics.

Prerequisites: COMP 251 , MATH 222 , MATH 223

Computer Science (Sci) : Fundamental mathematical and computational issues in computer animation with a focus on physics based simulation: overview of numerical integration methods, accuracy and absolute stability, stiff systems and constraints, rigid body motion, collision detection and response, friction, deformation, stable fluid simulation, use of motion capture, and other selected topics.

Prerequisite(s): MATH 222 , MATH 223 , COMP 206 , COMP 250

Computer Science (Sci) : Application of computer science techniques to problems arising in biology and medicine, techniques for modeling evolution, aligning molecular sequences, predicting structure of a molecule and other problems from computational biology. An in-depth exploration of key research areas.

Prerequisites: COMP 251 , and MATH 323 or MATH 203 or BIOL 309

Restriction: Not open to students who have taken or are taking COMP 462 .

Note: Additional work will consist of assignments and of a substantial final project that will require to put in practice the concepts covered in the course.

Computer Science (Sci) : Fundamental concepts and techniques in computational structural biology, system biology. Techniques include dynamic programming algorithms for RNA structure analysis, molecular dynamics and machine learning techniques for protein structure prediction, and graphical models for gene regulatory and protein-protein interaction networks analysis. Practical sessions with state-of-the-art software.

Prerequisite: COMP 462 .

Corequisite(s): COMP 462 or COMP 561

Computer Science (Sci) : Linear models in statistical genetics, causal inference, single-cell genomics, multi-omic learning, electronic health record mining. Applications of machine learning techniques: linear regression, latent factor models, variational Bayesian inference, neural networks, model interpretation.

Prerequisites: ( BIOL 202 or BIOL 302 ) and MATH 324 and ( COMP 451 or COMP 551 ), or equivalents.

Restrictions: Not open to students who have taken COMP 597 or COMP 598 when the topic was "Machine Learning in Genomics and Healthcare".

Computer Science (Sci) : Bandit algorithms, finite Markov decision processes, dynamic programming, Monte-Carlo Methods, temporal-difference learning, bootstrapping, planning, approximation methods, on versus off policy learning, policy gradient methods temporal abstraction and inverse reinforcement learning.

Prerequisite: A university level course in machine learning such as COMP 451 or COMP 551 . Background in calculus, linear algebra, probability at the level of MATH 222 , MATH 223 , MATH 323 , respectively.

Computer Science (Sci) : Practical aspects of building software systems with machine learning components: requirements, design, delivery, quality assessment, and collaboration. Consideration of a user-centered mindset in development; integration of design and development considerations relevant to artificial intelligence, such as security, privacy, and fairness.

Prerequisites: COMP 303 , COMP 424 or COMP 551

Restrictions: Not open to students who have taken COMP 598 or COMP 599 when the topic was "Software Engineering for Building Intelligent Systems".

Computer Science (Sci) : Representation, inference and learning with graphical models; directed and undirected graphical models; exact inference; approximate inference using deterministic optimization based methods, stochastic sampling based methods; learning with complete and partial observations.

Prerequisites: COMP 251 , MATH 323 , MATH 324 ; or equivalents.

Restrictions: Not open to students who have taken COMP 766 or COMP 767 when the topic was "Probabilistic Graphical Models".

A background in AI ( COMP 424 ) and machine learning ( COMP 451 or COMP 551 ) is highly recommended.

Computer Science (Sci) : Techniques related to microarrays (normalization, differential expression, class prediction, class discovery), the analysis of non-coding sequence data (identification of transcription factor binding sites), single nucleotide polymorphisms, the inference of biological networks, and integrative Bioinformatics approaches.

Prerequisite: Enrolment in Bioinformatics Option Program or permission of coordinators.

Restrictions: Enrolment by students in the Bioinformatics Option Program or by permission of course coordinators only. Computer Science graduate students not in the Bioinformatics Option Program need additional permission of the M.Sc. or Ph.D. Committee respectively.

Computer Science (Sci) : An overview of state-of-the-art algorithms used in machine learning, including theoretical properties and practical applications of these algorithms.

Prerequisites: COMP 424 , COMP 526 or ECSE 526 , COMP 360 , MATH 323 or ECSE 305 .

Computer Science (Sci) : Machine learning with graph-structured data. Introductions to spectral graph theory, graph signal processing, graph convolutions, graph neural networks, and the logic of graphs.

Prerequisites: MATH 222 , MATH 223 , COMP 360 , COMP 451 or COMP551 , or equivalents.

Restriction: Not open to students who have taken COMP 766 when the topic was "Graph Representation Learning".

Computer Science (Sci) : Advanced algorithms for the annotation of biological sequences. Algorithms and heuristics for pair-wise and multiple sequence alignment. Gene-finding with hidden Markov models and variants. Motifs discovery techniques: over representation and phylogenetic footprinting approaches. RNA secondary structure prediction. Detection of repetitive elements. Representation and annotation of protein domains.

Prerequisite: COMP 462 or with instructor's permission.

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