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Computer Science (COMP) Graduate Courses Listing

Computer Science 5011 Machine Learning and Neural Networks View Details
Covers the areas of basic artificial neural networks and designing shallow neural networks-based algorithms to solve practical problems. It aims to teach a machine to learn, to think, to analyze data, and to make an intelligent decision with related shallow neural networks. Involves state-of-the-art neural networks, including perceptron, Hopfield network, support vector machine, RBF network, random networks, recurrent network, and self-organizing maps for applications related to pattern recognition.
Credit Weight: 0.5
Offering: 3-0; or 3-0
Notes: This course is restricted to students enrolled in the MSc Computer Science

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Computer Science 5012 Big Data View Details
The explosion of social media and the computerization resulted in large volume of mostly unstructured data: web logs, videos, speech recordings, photographs, and e-mails. The key objective is for the students to learn and understand the most important technologies used in manipulating, storing, and analyzing Big Data. Many data analytics techniques will be introduced such as classification and clustering, decision trees, linear and logistic regression, time series analysis, and text analytics. Students will also learn how to select and apply the correct Big Data stores for disparate data sets and how to use proper data analytics techniques. Students will develop a variety of Big Data applications in their assignments and project practices.
Credit Weight: 0.5
Offering: 3-0; or 3-0

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Computer Science 5013 Pattern Recognition View Details
Focuses on fundamental concepts of characterizing and recognizing patterns which include Decision Theory, Probability Distributions, Linear Models, Neural Networks, Kernel Methods, Sparse Kernel Methods, Graphical Models, K-means Clustering, Mixture of Gaussians, Principal Component Analysis, and Independent Component Analysis. Discusses automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions. Applications of pattern recognition (face recognition, fingerprint recognition, number plate recognition, and speech recognition) will be discussed.
Credit Weight: 0.5
Offering: 3-0; or 3-0
Notes: This course is restricted to students enrolled in the MSc Computer Science

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Computer Science 5014 Natural Language Processing View Details
Introduces computational modelling of natural language processing (NLP). Topics covered include: language modelling, POS tagging, syntactic parsing, statistical parsing, lexical and compositional semantics, and discourse analysis. Introduces the application of machine learning algorithms and NLP for automatic summarisation of text, semantic analysis of sentences and social text analysis. Students will learn machine learning algorithms that are used in natural language processing.
Credit Weight: 0.5
Offering: 3-0; or 3-0
Notes: This course is restricted to students enrolled in the MSc Computer Science

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Computer Science 5015 Ethical issues in Computer Science View Details
Examines the ethical issues that arise as a result of increasing use of computers, the web and the responsibilities of those who work with computers and the internet, either as computer science professionals or end users. Students will explore some of the challenging ethical and philosophical issues such as privacy, intellectual property rights and proprietary software, security, accountability, liability, the digital divide, hacking, and viruses.
Credit Weight: 0.5
Grade Scheme: Pass/Fail
Offering: Spring or Summer term
Notes: Students that have previously taken Computer Science 5010 will not receive this course for credit.

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Computer Science 5111 Graduate Seminar View Details
Seminars covering areas of computer science are normally presented by the instructor(s), and by students who are asked to study and discuss a number of papers. The course will emphasize effective independent research approaches in computer science including literature searches, discussion and presentation of research material, and the ability to identify possible new areas for investigation.
Credit Weight: 0.5
Offering: 3-0; or 3-0
Notes: Students who have previous credit in Computer Science 5400 may not take Computer Science 5111 for credit.

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Computer Science 5112 Research Methodology in Computer Science View Details
Provides an overview of recent research in computer science and introduces several advanced research topics depending on the research of the instructor. Students will be exposed to basic knowledge and skills about conducting scientific research, which includes critical and creative thinking, qualitative and quantitative research approaches, research life-cycle and time management. Students will complete a research review paper on a select topic.
Credit Weight: 0.5
Offering: 3-0; or 3-0

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Computer Science 5211 Object Oriented Programming View Details
Students are taught to program well in an object-oriented style. The focus is more on object-oriented design and programming than on a particular language and its niceties. Topics covered will include OO design, test-driven development, refactoring, reuse, aspect-oriented, parameterization, distribution, inheritance and programming design patterns.
Credit Weight: 0.5
Offering: 3-0; or 3-0
Notes: Students who have previous credit in Computer Science 5401 may not take Computer Science 5211 for credit.

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Computer Science 5212 Programming Languages View Details
An examination of one or more of the following topics at an advanced level: logic programming, functional programming, abstract machines, declarative semantics, design and implementation issues.
Credit Weight: 0.5
Offering: 3-0; or 3-0
Notes: Students who have previous credit in Computer Science 5410 may not take Computer Science 5212 for credit.

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Computer Science 5213 Computer Networks View Details
Students learn how to improve the performance of networks in various ways such as congestion control, call admission control, routing techniques, QoS enhancement. Internetworking and real time multimedia transmission problems will be considered. Simulation and/or experiments will be used to verify and compare the proposed techniques. Attention will be paid to wireless networks (e.g. mobile IP, Ad Hoc networks).
Credit Weight: 0.5
Offering: 3-0; or 3-0

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Computer Science 5311 Applied Combinatorics View Details
Topics are examined in one of the following areas: applied graph theory, combinatorial designs and its application in coding theory, combinatorial algorithms in enumeration and search.
Credit Weight: 0.5
Offering: 3-0; or 3-0

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Computer Science 5312 Scientific and Parallel Computing View Details
Scientific computing topics from areas such as computational linear algebra, differential equations, multi scale methods, scattering problems and image processing are examined in the context of parallel algorithms. A significant part of the course will involve the use of parallel computing resources.
Credit Weight: 0.5
Offering: 3-0; or 3-0

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Computer Science 5313 Artificial Intelligence View Details
Deals with a broad range of advanced topics in artificial intelligence (AI). A specific emphasis will be on the logical reasoning, statistical and decision-theoretic modeling paradigm. It enables graduate students to build autonomous intelligent agents that efficiently make decisions in fully informed, partially observable and adversarial settings. The intelligent agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. The algorithms and techniques introduced in this course will enable the grad student to apply it to a wide variety of artificial intelligence problems and will serve as a foundation for further research in any application areas.
Credit Weight: 0.5
Offering: 3-0; or 3-0
Notes: Students who have previous credit in Computer Science 5415 may not take Computer Science 5313 for credit.

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Computer Science 5411 Advanced Topics in Computer Science View Details
Selected topics in computer science that will be designated by the Department on a case by case basis to fall within one of the Computer Science Course Groupings (see Computer Science Graduate Programs).
Credit Weight: 0.5
Prerequisite(s): Permission of the Department
Special Topic: Yes
Offering: 3-0; 0-0

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Computer Science 5413 Advanced Topics in Computer Science View Details
Selected topics in computer science that will be designated by the Department on a case by case basis to fall within one of the Computer Science Course Groupings (see Computer Science Graduate Programs).
Credit Weight: 0.5
Special Topic: Yes
Offering: 0-0; 3-0

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Computer Science 5421 Deep Learning View Details
Covers several advanced topics in artificial intelligence and the design of machine learning based algorithms to solve practical problems. Involves state-of-the-art neural networks, including deep learning, sequential learning, convolutional neural networks, extreme learning machine, etc. for applications related to pattern recognition.
Credit Weight: 0.5
Offering: 3-0; or 3-0

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Computer Science 5422 Computer Vision and Image Analysis View Details
Focuses on fundamental concepts of computer vision, image processing, and image analysis. Topics may include image perception, sampling and quantization, transforms, filtering, background and object segmentation, edge detection, feature extraction, shape representation and description, object tracking, classification, and popular machine learning techniques.
Credit Weight: 0.5
Offering: 3-0; or 3-0

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Computer Science 5435 Reading Course View Details
Directed studies in an area of computer science that will be designated by the Department on a case by case basis to fall within one of the Computer Science Course Groupings (see Computer Science Graduate Programs).
Credit Weight: 0.5
Special Topic: Yes
Offering: 3-0; 0-0

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Computer Science 5437 Reading Course View Details
Directed studies in an area of computer science that will be designated by the Department on a case by case basis to fall within one of the Computer Science Course Groupings (see Computer Science Graduate Programs).
Credit Weight: 0.5
Special Topic: Yes
Offering: 3-0; or 3-0

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Computer Science 5450 Mobile Programming View Details
Students will learn how to program high quality real-world mobile, Web and native applications and solutions using a variety of technologies and programming languages including Sencha Touch, JQuery Mobile, Xamarin, Objective-C, Cordova for a variety of leading mobile devices. Students will study the design of user interfaces and software systems using the most common languages and frameworks and relate to associated topics such as mobile gaming, hosting infrastructure, and security.
Credit Weight: 0.5
Offering: 3-0; or 3-0

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Computer Science 5451 Advanced Multimedia Programming View Details
Efficient programming practices for creating digital media products as well as creating interactive applications using Java, Java3D, Java Media Framework and other design tools. Animation, computer games, Web and sound technology will be studied.
Credit Weight: 0.5
Offering: 3-0; or 3-0

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Computer Science 5472 Computer Graphics View Details
An examination of one or more of the following topics at an advanced level: surface representation, ray tracing, rendering, image processing, animation.
Credit Weight: 0.5
Offering: 3-0; or 3-0
Notes: Students who have previous credit in Computer Science 5471 may not take Computer Science 5472 for credit.

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Computer Science 5473 Computer Security View Details
Several important research topics in one or more of the following areas are investigated: cryptography, computer network security, data security and information security.
Credit Weight: 0.5
Offering: 3-0; or 3-0

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Computer Science 5800 (9800) Project View Details
A full year course of directed research that may involve any combination of theory or application in an area of computer science, as agreed to by the student and the research project supervisor in a "learning contract", which states what is to be done in the research project, how and when it will be done, and how it will be evaluated. A significant portion of the work will involve the preparation of a written report along with a public presentation.
Credit Weight: 1.0

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Computer Science 5901 (9901) Master's Thesis View Details
Credit Weight: 2.0
Grade Scheme: Pass/Fail

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Computer Science 5990 Co-op Work Term I View Details
Credit Weight: 0.5

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Computer Science 5991 Co-op Work Term II View Details
Credit Weight: 0.5

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