83 undergraduate courses in the 2026–27 Undergraduate Calendar, and 97 graduate courses in the Fall 2026 Graduate Calendar.
Undergraduate
CS 100Introduction to Computing Through Applications0.50 units
Using personal computers as effective problem solving tools for the present and the future. Effective use of spreadsheets to process, manipulate, and visualize numeric and textual information. Introduction to the Internet, World Wide Web, HTML, and XML. Algorithms underlying the functional components of web search engines and their influence on data access. Using wikis to publish, reshape, and organize data collaboratively.
Antirequisites
Complete all of the following
Not completed nor concurrently enrolled in: All 200-, 300-, 400-level CS courses or equivalents
CS 105Introduction to Computer Programming 10.50 units
An introduction to the fundamentals of computer programming through media computation. Students will learn to write interactive graphical programs. Fundamental language concepts such as variables, conditionals, loops, functions, and arrays. Programming concepts such as coding style, modular design, testing, and debugging. Media concepts such as 2D graphics drawing, input, animation, and image processing.
Antirequisites
Complete all of the following
Not completed nor concurrently enrolled in any of the following:
CS 106Introduction to Computer Programming 20.50 units
A continuation of the introduction to computer programming begun in CS 105. The use of programming, in conjunction with libraries, as a means of solving practical problems in art, design, and data processing. Basic text processing, manipulation of images and sound, handling and visualization of tabular and hierarchical data. Introductions to user interfaces, physical simulation, and object-oriented programming.
Prerequisites
Must have completed the following:
CS105 - Introduction to Computer Programming 1 (0.50)
Antirequisites
Complete all of the following
Not completed nor concurrently enrolled in any of the following:
CS 114Principles of Computing for Science0.50 units
Introduction to basic imperative programming principles; programming concepts including functions, flow control, lists, arrays; numerical accuracy and efficiency; data analysis and general-purpose algorithms. Introduction to object-oriented programming concepts.
Antirequisites
Complete all of the following
Not completed nor concurrently enrolled in any of the following:
CS 115Introduction to Computer Science 10.50 units
An introduction to the fundamentals of computer science through the application of elementary programming patterns in the functional style of programming. Function definition and application. Tracing via substitution. Design, testing, and documentation. Recursive data definitions. Lists and trees. Functional and data abstraction.
Antirequisites
Complete all of the following
Not completed nor concurrently enrolled in any of the following:
CS 116Introduction to Computer Science 20.50 units
This course builds on the techniques and patterns learned in CS 115 while making the transition to use of an imperative language. Generative and structural recursion. Mutation (assignment) and its role in an imperative language. Primitive types and basic I/O. Sequencing, selection, looping. Function definition and use. File and console I/O. Issues in computer science.
An introduction to the fundamentals of computer science through the application of elementary programming patterns in the functional style of programming. Syntax and semantics of a functional programming language. Tracing via substitution. Design, testing, and documentation. Linear and nonlinear data structures. Recursive data definitions. Abstraction and encapsulation. Generative and structural recursion. Historical context.
Antirequisites
Complete all of the following
Not completed nor concurrently enrolled in any of the following:
CS 136Elementary Algorithm Design and Data Abstraction0.50 units
This course builds on the techniques and patterns learned in CS 135 while making the transition to use an imperative language. It introduces the design and analysis of algorithms, the management of information, and the programming mechanisms and methodologies required in implementations. Topics discussed include iterative and recursive sorting algorithms; lists, stacks, queues, trees, and their application; abstract data types and their implementations.
CS 136LTools and Techniques for Software Development0.25 units
This course introduces students to tools and techniques useful in the software development lifecycle. Students learn to navigate and leverage commands and utilities in the Linux Command Line Shell. Students gain experience in version control software, writing scripts to automate tasks, and creating effective test cases to identify bugs. Tracing and debugging strategies are discussed. Students also gain experience in using built-in support for version control, testing, debugging, build automation, etc. in integrated development environments (IDEs).
Review of fundamental programming concepts and their application. Procedures and parameter passing. Arrays and structures. Recursion. Sorting. Pointers and simple dynamic structures. Space and time analysis of designs. Design methodologies.
CS 138Introduction to Data Abstraction and Implementation0.50 units
Software abstractions via elementary data structures and their implementation; encapsulation and modularity; class and interface definitions; object instantiation; recursion; elementary abstract data types, including sequences, stacks, queues, and trees; implementation using linked structures and arrays; vectors and strings; memory models; automatic vs. dynamic memory management.
CS 200Concepts for Advanced Computer Usage0.50 units
Important concepts underlying major personal computer application categories; methodologies for learning and evaluating software; operating system and hardware design from the user's point of view, with implications for maintaining a personal computer. Students are encouraged to use their own personal computer for assignments. Social media and the effect of technology on society are explored throughout the course.
The study of the steps required to solve real-world problems on a computer, including problem specification, choice of algorithmic paradigm, analysis, and implementation. Topics include exhaustive search, divide and conquer, greedy, and dynamic programming approaches.
Top-down design of data structures. Using representation-independent data types. Introduction to commonly used data types, including lists, sets, mappings, and trees. Selection of data representation.
CS 240Data Structures and Data Management0.50 units
Introduction to widely used and effective methods of data organization, focusing on data structures, their algorithms, and the performance of these algorithms. Specific topics include priority queues, sorting, dictionaries, data structures for text processing.
CS 241Foundations of Sequential Programs0.50 units
The relationship between high-level languages and the computer architecture that underlies their implementation, including basic machine architecture, assemblers, specification and translation of programming languages, linkers and loaders, block-structured languages, parameter passing mechanisms, and comparison of programming languages.
Prerequisites
Complete all of the following
Complete 1 of the following
Must have completed the following:
CS138 - Introduction to Data Abstraction and Implementation (0.50)
Complete all of the following
Must have completed the following:
CS136L - Tools and Techniques for Software Development (0.25)
Earned a minimum grade of 85% in at least 1 of the following:
CS136 - Elementary Algorithm Design and Data Abstraction (0.50)
CS146 - Elementary Algorithm Design and Data Abstraction (Advanced Level) (0.50)
Complete all of the following
Must have completed the following:
CS136L - Tools and Techniques for Software Development (0.25)
Must have completed at least 1 of the following:
CS246 - Object-Oriented Software Development (0.50)
CS246E - Object-Oriented Software Development (Enriched) (0.50)
Logic as a tool for representation, reasoning, and computation. Propositional and predicate logic. Formalizing the notions of correct and incorrect reasoning, defining what is computable, and exploring the limits of computation. Godel's Incompleteness Theorem. Applications of logic to computer science.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
CS136 - Elementary Algorithm Design and Data Abstraction (0.50)
CS138 - Introduction to Data Abstraction and Implementation (0.50)
CS146 - Elementary Algorithm Design and Data Abstraction (Advanced Level) (0.50)
CS 246Object-Oriented Software Development0.50 units
Introduction to object-oriented programming and to tools and techniques for software development. Designing, coding, debugging, testing, and documenting medium-sized programs: reading specifications and designing software to implement them; selecting appropriate data structures and control structures; writing reusable code; reusing existing code; basic performance issues; debuggers; test suites.
Prerequisites
Complete all of the following
Complete 1 of the following
Must have completed the following:
CS136L - Tools and Techniques for Software Development (0.25)
CS146 - Elementary Algorithm Design and Data Abstraction (Advanced Level) (0.50)
Earned a minimum grade of 60% in each of the following:
CS138 - Introduction to Data Abstraction and Implementation (0.50)
Complete all of the following
Must have completed the following:
CS136L - Tools and Techniques for Software Development (0.25)
Earned a minimum grade of 60% in each of the following:
CS136 - Elementary Algorithm Design and Data Abstraction (0.50)
Enrolled in an Honours Mathematics program
Antirequisites
Complete all of the following
Not completed nor concurrently enrolled in any of the following:
CS246E - Object-Oriented Software Development (Enriched) (0.50)
Systematic methods for designing, coding, testing, and documenting medium-sized programs. Major topics include abstraction, modularity, software modelling, object-oriented programming and design, generic programming, testing and debugging.
Overview of computer organization and performance. Basics of digital logic design. Combinational and sequential elements. Data representation and manipulation. Basics of processor design. Pipelining. Memory hierarchies. Multiprocessors.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
CS136 - Elementary Algorithm Design and Data Abstraction (0.50)
CS138 - Introduction to Data Abstraction and Implementation (0.50)
CS146 - Elementary Algorithm Design and Data Abstraction (Advanced Level) (0.50)
An introduction to information systems and their strategic role in business. Topics include types of information systems, organizational requirements, systems development strategies, decision support systems, data and information management, and information systems management, control, and implementation.
Prerequisites
Complete all of the following
Complete 1 of the following
Must have completed at least 1 of the following:
CS106 - Introduction to Computer Programming 2 (0.50)
CS 335Computational Methods in Business and Finance0.50 units
An introduction to numerical methods for business and finance. Floating-point arithmetic, interpolation. Methods for portfolio optimization and contingent-claims valuation. Solution of nonlinear equations. Monte Carlo methods, lattice methods, simulation of hedging strategies.
CS 338Computer Applications in Business: Databases0.50 units
A user-oriented approach to the management of large collections of data. Methods used for the storage, selection, and presentation of data. Common database management systems.
Prerequisites
Complete 1 of the following
Must have completed at least 1 of the following:
CS230 - Introduction to Computers and Computer Systems (0.50)
The study of efficient algorithms and effective algorithm design techniques. Program design with emphasis on pragmatic and mathematical aspects of program efficiency. Topics include divide and conquer algorithms, recurrences, greedy algorithms, dynamic programming, graph search and backtrack, problems without algorithms, NP-completeness and its implications.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
CS240 - Data Structures and Data Management (0.50)
CS240E - Data Structures and Data Management (Enriched) (0.50)
CS 343Concurrent and Parallel Programming0.50 units
An introduction to concurrent and parallel programming, with an emphasis on language constructs. Major topics include exceptions, coroutines, atomic operations, critical sections, mutual exclusion, semaphores, high-level concurrency, deadlock, interprocess communication, process structuring, shared memory, and distributed architectures. Students will learn how to structure, implement, and debug concurrent programs.
Introduction to full-stack application design and development. Students will work in project teams to design and build complete, working applications and services using standard tools. Topics include best-practices in design, development, testing, and deployment.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
CS246 - Object-Oriented Software Development (0.50)
CS246E - Object-Oriented Software Development (Enriched) (0.50)
CS 348Introduction to Database Management0.50 units
The main objective of this course is to introduce students to fundamentals of database technology by studying databases from three viewpoints: those of the database user, the database designer, and the database administrator. It teaches the use of a database management system (DBMS) by treating it as a black box, focusing only on its functionality and its interfaces. Topics include introduction to database systems, relational database systems, database design methodology, SQL and interfaces, database application development, concept of transactions, ODBC, JDBC, database tuning, database administration, and current topics (distributed databases, data warehouses, data mining).
An introduction to contemporary user interface implementation concepts, including event abstraction, graphical components, layout, feedback, testing, accessibility, and architectures to develop user interfaces. One or more types of interface toolkit paradigms are considered.
An introduction to the fundamentals of operating system function, design, and implementation. Topics include concurrency, synchronization, processes, threads, scheduling, memory management, file systems, device management, and security.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
CS240 - Data Structures and Data Management (0.50)
CS240E - Data Structures and Data Management (Enriched) (0.50)
CS 360Introduction to the Theory of Computing0.50 units
Models of computers including finite automata and Turing machines. Basics of formal languages with applications to the syntax of programming languages. Alternate characterizations of language classes. Proving unrecognizability. Unsolvable problems and their relevance to the semantics of programming.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
CS240 - Data Structures and Data Management (0.50)
CS240E - Data Structures and Data Management (Enriched) (0.50)
Finite automata and regular expressions. Pushdown automata and context-free grammars. Turing machines and undecidability. Time and space complexity. Diagonalization and hierarchies. CS 365 covers the material in CS 360 at an accelerated pace plus additional topics in computational complexity.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
CS240 - Data Structures and Data Management (0.50)
CS240E - Data Structures and Data Management (Enriched) (0.50)
Principles and practices of basic numerical computation as a key aspect of scientific computation. Visualization of results. Approximation by splines, fast Fourier transforms, solution of linear and nonlinear equations, differential equations, floating point number systems, error, stability. Presented in the context of specific applications to image processing, analysis of data, scientific modelling.
CS 371Introduction to Computational Mathematics0.50 units
A rigorous introduction to the field of computational mathematics. The focus is on the interplay between continuous models and their solution via discrete processes. Topics include pitfalls in computation, solution of linear systems, interpolation, discrete Fourier transforms, and numerical integration. Applications are used as motivation.
An upper-level studio course to create computational projects that function as art works and aesthetic experiences. Students will work in an interdisciplinary environment to combine computer science principles with fine art technical and conceptual skills.
Prerequisites
Complete all of the following
Must have completed the following:
CS240 - Data Structures and Data Management (0.50)
CS240E - Data Structures and Data Management (Enriched) (0.50)
An investigation into the role and function of software engineering practice in the construction of computer based systems. Topics include: requirements and specification; documentation techniques; analysis and design; implementation; testing and maintenance; management issues.
CS 431Data-Intensive Distributed Analytics0.50 units
Introduces non-CS major students to infrastructure for data-intensive analytics, with a focus on abstractions, frameworks, and algorithms that allow developers to distribute computation across many machines. Topics include core concepts (partitioning, replication, locality, consistency), computational models (MapReduce, dataflows, stream processing, bulk-synchronous parallel), and applications.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
CS230 - Introduction to Computers and Computer Systems (0.50)
CS 436Networks and Distributed Computer Systems0.50 units
An introduction to networks, protocols, and distributed systems. Layered models, resource management, naming, addressing and routing, reliable communication, security, and higher-level services.
Prerequisites
Must have completed at least 1 of the following:
CS230 - Introduction to Computers and Computer Systems (0.50)
CS 442Principles of Programming Languages0.50 units
An exposure to important concepts and issues in contemporary programming languages. Data types, abstraction, and polymorphism. Program structure. Lambda calculus and functional programming, logic programming, object-oriented programming. Semantics of programming languages. Critical comparison of language features and programming methodologies using examples drawn from a variety of programming languages including Lisp, Prolog, ML, Ada, Smalltalk, Icon, APL, and Lucid. Programming assignments involve the use of some of these languages.
Phases of compilation. Lexical analysis and a review of parsing. Compiler-compilers and translator writing systems. LEX and YACC. Scope rules, block structure, and symbol tables. Runtime stack management. Parameter passage mechanisms. Stack storage organization and templates. Heap storage management. Intermediate code. Code generation. Macros.
CS 445Software Requirements Specification and Analysis0.50 units
Introduces students to the requirements definition phase of software development. Models, notations, and processes for software requirements identification, representation, analysis, and validation. Cost estimation from early documents and specifications.
Introduces students to the design, implementation, and evolution phases of software development. Software design processes, methods, and notation. Implementation of designs. Evolution of designs and implementations. Management of design activities.
CS 447Software Testing, Quality Assurance, and Maintenance0.50 units
Introduces students to systematic testing of software systems. Software verification, reviews, metrics, quality assurance, and prediction of software reliability and availability. Related management issues.
The objective of this course is to introduce students to fundamentals of building a relational database management system. The course focuses on the database engine core technology by studying topics such as storage management (data layout, disk-based data structures), indexing, query processing algorithms, query optimization, transactional concurrency control, logging and recovery.
An introduction to the fundamental theories, methods, and research in the design and evaluation of novel computational artifacts designed to meet real-world human needs.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
CS240 - Data Structures and Data Management (0.50)
CS240E - Data Structures and Data Management (Enriched) (0.50)
The course is intended to provide the student with an appreciation of modern computer design and its relation to system architecture, compiler technology, and operating system functionality. The course places an emphasis on design based on the measurement of performance and its dependency on parallelism, efficiency, latency, and resource utilization.
CS 451Data-Intensive Distributed Computing0.50 units
Introduces students to infrastructure for data-intensive computing, with a focus on abstractions, frameworks, and algorithms that allow developers to distribute computations across many machines. Topics include core concepts (partitioning, replication, locality, consistency), computational models (MapReduce, dataflows, stream processing, bulk-synchronous parallel), and applications.
Intended to give students experience with tools and techniques of real-time programming, this course includes not only issues of microcomputer architecture and a real-time programming language and operating system, but also hands-on experience programming a microcomputer for applications such as process control, data acquisition and communication.
Introduction to security issues in modern software, operating systems, and other computing platforms (e.g., mobile and cloud environments). Causes of security breaches and methods to help detect, isolate, and prevent them. Specific topics include comparing security and privacy, program security, operating system security, mobile security, hardware security, administrating security, and legal and ethical issues.
An introduction to distributed systems, emphasizing the multiple levels of software in such systems. Specific topics include fundamentals of data communications, network architecture and protocols, local-area networks, concurrency control in distributed systems, recovery in distributed systems, and clock synchronization.
An introduction to network architectures and protocols, placing emphasis on protocols used in the Internet. Specific topics include application layer protocols, network programming, transport protocols, routing, multicast, data link layer issues, multimedia networking, network security, and network management.
Basic techniques of system performance evaluation. Specific topics include performance modelling, discrete event simulation, verification and validation of simulation models, analysis of simulation output, analysis of single server queue and queueing networks, modelling of computer systems, networks, and other queueing or non-queueing systems.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
CS246 - Object-Oriented Software Development (0.50)
CS246E - Object-Oriented Software Development (Enriched) (0.50)
CS 459Privacy, Cryptography, Network and Data Security0.50 units
Introduction to privacy and security using cryptography and related techniques in networks, distributed systems, and data science. The course examines how data and metadata can be protected at rest, in transit, and during computation. For at-rest protection, specific topics include the basics of cryptography and relevant ethics/policy concepts. For in-transit protection, specific topics include network defenses, authentication, and secure and anonymous communication protocols. For during-computation protection, specific topics include data inference, differential privacy, homomorphic encryption, multi-party computations, and related protocols.
Languages and their representations. Grammars --Chomsky hierarchy. Regular sets and sequential machines. Context-free grammars -- normal forms, basic properties. Pushdown automata and transducers. Operations on languages. Undecidable problems in language theory. Applications to the design of programming languages and compiler construction.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
CS360 - Introduction to the Theory of Computing (0.50)
Algorithmic approaches and methods of assessment that reflect a broad spectrum of criteria, including randomized algorithms, amortized analysis, lower bounds, approximation algorithms, and on-line algorithms. Particular examples will be chosen from different areas of active research and application.
Basic concepts and implementation of numerical linear algebra techniques and their use in solving application problems. Special methods for solving linear systems having special features. Direct methods: symmetric, positive definite, band, general sparse structures, ordering methods. Iterative methods: Jacobi, Gauss-Seidel, SOR, conjugate gradient. Computing and using orthogonal factorizations of matrices. QR and SVD methods for solving least squares problems. Eigenvalue and singular value decompositions. Computation and uses of these decompositions in practice.
Prerequisites
Must have completed at least 1 of the following:
AMATH242 - Introduction to Computational Mathematics (0.50)
CS 476Numerical Computation for Financial Modelling0.50 units
The interaction of financial models, numerical methods, and computing environments. Basic computational aspects of option pricing and hedging. Numerical methods for stochastic differential equations, strong and weak convergence. Generating correlated random numbers. Time-stepping methods. Finite difference methods for the Black-Scholes equation. Discretization, stability, convergence. Methods for portfolio optimization, effect of data errors on portfolio weights.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
AMATH242 - Introduction to Computational Mathematics (0.50)
An introduction to neural network methods, with some discussion of their relation to neuroscience. Simple neuron models and networks of neurons. Training feedforward networks. Backpropagation of errors and stochastic gradient descent. Unsupervised learning methods. Recurrent neural networks. Convolutional neural networks. Continuous time models. Adversarial attacks. Advanced topics may include neural engineering, biologically plausible learning methods.
Introduction to modelling and algorithmic techniques for machines to learn concepts from data. Generalization: underfitting, overfitting, cross-validation. Tasks: classification, regression, clustering. Optimization-based learning: loss minimization. regularization. Statistical learning: maximum likelihood, Bayesian learning. Algorithms: nearest neighbour, (generalized) linear regression, mixtures of Gaussians, Gaussian processes, kernel methods, support vector machines, deep learning, sequence learning, ensemble techniques. Large scale learning: distributed learning and stream learning. Applications: Natural language processing, computer vision, data mining, human computer interaction, information retrieval.
CS 482Computational Techniques in Biological Sequence Analysis0.50 units
Computer science principles and algorithms in biological sequence analysis. Topics include algorithms for sequence comparison, for large-scale database search in biological databases, for sequence assembly, for evolutionary tree reconstruction, for identifying important features in DNA and RNA sequences, and underlying computational techniques for understanding strings and trees and for making probabilistic inferences.
Introduction to image and vision understanding by computer. Camera-system geometry, image formation and lighting, and image acquisition. Basic visual processes for recognition of edges, regions, lines, and surfaces. Processing of stereo images, and motion in image sequences. Object recognition. Applications of computer-vision systems.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
AMATH242 - Introduction to Computational Mathematics (0.50)
CS 485Statistical and Computational Foundations of Machine Learning0.50 units
Extracting meaningful patterns from random samples of large data sets. Statistical analysis of the resulting problems. Common algorithmic paradigms for such tasks. Central concepts: VC-dimension, margins of a classifier, sparsity and description length, other types of regularization. Performance guarantees: generalization bounds, data dependent error bounds, and computational complexity of learning algorithms. Common paradigms: neural networks, kernel methods and support-vector machines, boosting, nearest neighbor classifiers. Applications to data mining.
CS 486Introduction to Artificial Intelligence0.50 units
Goals and methods of artificial intelligence. Methods of general problem solving. Knowledge representation and reasoning. Planning. Reasoning about uncertainty. Machine learning. Multi-agent systems. Natural language processing.
CS 487Introduction to Symbolic Computation0.50 units
An introduction to the use of computers for symbolic mathematical computation, involving traditional mathematical computations such as solving linear equations (exactly), analytic differentiation and integration of functions, and analytic solution of differential equations.
Software and hardware for interactive computer graphics. Implementation of device drivers, 3-D transformations, clipping, perspective, and input routines. Data structures, hidden surface removal, colour shading techniques, and some additional topics will be covered.
The integration of business and technical considerations in the design, implementation and management of information systems. Topics include: IS planning and development; business, management, executive, and strategic information systems, including case studies of selected large- scale systems; decision support systems; end-user training and development; systems security, disaster planning and recovery. Practical examples of information systems in industry.
CS 492The Social Implications of Computing0.50 units
This course is designed to consider the problems encountered by individuals, organizations and society as computer technology is adopted, with a view towards assessing possible courses of action.
Prerequisites
Complete all of the following
Must have completed at least 1 of the following:
CS240 - Data Structures and Data Management (0.50)
CS240E - Data Structures and Data Management (Enriched) (0.50)
Students work in teams on substantial open-ended computer science problems as part of the CS 493/494 course sequence. Lectures describe project management fundamentals and ethical and legal issues in computing. Students form teams, select projects, define project goals, perform risk assessment, establish a project plan, and develop a prototype. Possible project topics can include development of software systems, analysis of extensions to existing systems across the field, and experimental computer science.
Continuing from CS 493, student teams continue development of their project, update project plans, explore design alternatives, perform testing, and analyze experimental results. Teams prepare and deliver technical presentations and demonstrations of their projects, and analyze ethical and legal aspects of their work.
The student will undertake new analysis, synthesis, measurement, or experimentation to produce a document that demonstrates a depth of understanding of a topic that goes beyond what is obtained in a standard undergraduate education.
A detailed thesis proposal and names of a supervisor and a reader are required.
CS 600Fundamentals of Computer Science for Data Science0.50 units
This course is designed for Data Science MMath students who do not have an undergraduate degree in Computer Science. It provides these students with the background needed to take Data Science graduate courses with a strong computational flavour. Topics include the following. The design and operation of processors. Memory hierarchies. Basic concepts of operating systems, concurrency, parallelism, and networks. Elementary data structures and their use in algorithm design and analysis.
CS 631Data-Intensive Distributed Analytics0.50 units
Introduces non-CS major students to infrastructure for data-sensitive analytics, with a focus on abstractions, frameworks, and algorithms that allow developers to distribute computations across many machines. Topics include core concepts (partitioning, replication, locality, consistency), computational models (MapReduce, dataflows, stream processing, bulk-synchronous parallel), and applications.
CS 634Security and Privacy in Health Systems0.50 units
An overview of basic security and privacy principles relevant in the design and use of applications in health settings. Program security, operating system security, network security, data security, and issues related to the management of security and privacy policies are introduced. Master of Health Informatics students only.
CS 636Introduction to Computer Networks and Distributed Computer Systems0.50 units
An introduction to computer networks, Internet protocols, and distributed systems. Layered models, naming, addressing and routing, reliable communication, security, and elements of distributed system design. Master of Health Informatics students only.
CS 638Principles of Data Management and Use0.50 units
A user-oriented approach to data management focusing on database principles, how to design a database, how to access data in the database and some recent technology for big data processing and the NoSQL movement. Relational database technology, relational algebra, SQL, database views, transactions, data modeling methodology, entity-relationship models. Introduction to several current topics in database research, such as warehousing, data mining, big data processing, NoSQL systems. Master of Health Informatics and Analytics students only.
CS 642Principles of Programming Languages0.50 units
An exposure to important concepts and issues in contemporary programming languages. Data types, abstraction, and polymorphism. Program structure. Lambda calculus and functional programming, logic programming, object-oriented programming. Semantics of programming languages. Critical comparison of language features and programming methodologies using examples drawn from a variety of programming languages including Lisp, Prolog, ML, Ada, Smalltalk, Icon, APL, and Lucid. Programming assignments involve the use of some of these languages.
Phases of compilation. Lexical analysis and a review of parsing. Compiler-compilers and translator writing systems. LEX and YACC. Scope rules, block structure, and symbol tables. Runtime stack management. Parameter passage mechanisms. Stack storage organization and templates. Heap storage management. Intermediate code. Code generation. Macros.
CS 645Software Requirements Specification and Analysis0.50 units
Introduces students to the requirements definition phase of software development: Models, notations, and processes for software requirements identification, representation, analysis, and validation. Cost estimation from early documents and specifications.
Introduces students to the design, implementation, and evolution phases of software development. Software design processes, methods, and notation. Implementation of designs. Evolution of designs and implementations. Management of design activities.
CS 647Software Testing, Quality Assurance and Maintenance0.50 units
Introduces students to systematic testing of software systems. Software verification, reviews, metrics, quality assurance, and prediction of software reliability and availability. Related management issues.
The objective of this course is to introduce students to fundamentals of building a relational database management system. The course focuses on the database engine core technology by studying topics such as storage management (data layout, disk-based data structures), indexing, query processing algorithms, query optimization, transactional concurrency control, logging and recovery. Preference will be given to CS graduate students. All other require permission from the school.
An introduction to the fundamental theories, methods, and research in the design and evaluation of description: novel computational artifacts designed to meet real-world human needs.
The course is intended to provide the student with an appreciation of modern computer design and its relation to system architecture, compiler technology and operating system functionality. The course places an emphasis on design based on the measurement of performance and its dependency on parallelism, efficiency, latency and resource utilization.
CS 651Data-Intensive Distributed Computing0.50 units
Introduces students to infrastructure for data-intensive computing, with a focus on abstractions, frameworks, and algorithms that allow developers to distribute computations across many machines. Topics include core concepts (partitioning, replication, locality, consistency), computational models (MapReduce, dataflows, stream processing, bulk-synchronous parallel), and applications.
Intended to give students experience with tools and techniques of real-time programming, this course includes not only issues of microcomputer architecture and a real-time programming language and operating system, but also hands-on experience programming a microcomputer for applications such as process control, data acquisition and communication. .Preference will be given to CS graduate students. All others require approval from the department.
Introduction to security issues in modern software, operating systems, and other computing platforms (e.g., mobile and cloud environments). Causes of security breaches and methods to help detect, isolate, and prevent them. Specific topics include comparing security and privacy, program security, operating system security, mobile security, hardware security, administrating security, and legal and ethical issues. Knowledge of operating systems equivalent to that obtained from CS 350 is assumed.
An introduction to distributed systems, emphasizing the multiple levels of software in such systems. Specific topics include fundamentals of data communications, network architecture and protocols, local-area networks, concurrency control in distributed systems, recovery in distributed systems, and clock synchronization. Preference will be given to CS graduate students. All others require approval from the department.
Antirequisites
Not completed nor concurrently enrolled in any of the following:
CS755 - System and Network Architectures and Implementation (0.50)
An introduction to network architectures and protocols, placing emphasis on protocols used in the Internet. Specific topics include application layer protocols, network programming, transport protocols, routing, multicast, data link layer issues, multimedia networking, network security, and network management.
Antirequisites
Not completed nor concurrently enrolled in any of the following:
CS755 - System and Network Architectures and Implementation (0.50)
Basic techniques of system performance evaluation. Specific topics include: performance modeling, discrete event simulation, verification and validation of simulation models, analysis of simulation output, analysis of single server queue and queueing networks, modeling of computer systems, networks, and other queueing or non-queueing systems.
CS 659Privacy, Cryptography, Network and Data Security0.50 units
Introduction to privacy and security using cryptography and related techniques in networks, distributed systems, and data science. The course examines how data and metadata can be protected at rest, in transit, and during computation. For at-rest protection, specific topics include the basics of cryptography and relevant ethics/policy concepts. For in-transit protection, specific topics include network defenses, authentication and secure and anonymous communication protocols. For during-computation protection, specific topics include data inference, differential privacy, homomorphic encryption, multi-party computations, and related protocols. Knowledge of distributed systems and computer networks equivalent to that obtained from CS 454 and CS 456 is assumed.
Languages and their representations. Grammars-Chomsky hierarchy. Regular sets and sequential machines. Context-free grammars-normal forms, basic properties. Pushdown automata and transducers. Operations on languages. Undecidable problems in language theory. Applications to the design of programming languages and compiler construction.
The classification of problems according to the computational resources required for their solution, with emphasis on properties of feasible computations rather than on specific algorithms. Topics include: time and space complexity, tractable and intractable problems, computation using randomness, parallel computation.
Design of good algorithms and analysis of the resources they consume. Lower bounds on the resource requirements of algorithms to compute certain functions. Problems from the following areas are discussed in this light: sorting and order statistics, data structures, arithmetic computations, the NP-complete problems.
Basic concepts and implementation of numerical linear algebra techniques and their use in solving application problems. Special methods for solving linear systems having special features. Direct methods: symmetric, positive definite, band, general sparse structures, ordering methods. Iterative methods: Jacobi, Gauss-Seidel, SOR, conjugate gradient. Computing and using orthogonal factorizations of matrices. QR and SVD methods for solving least squares problems. Eigenvalue and singular value decompositions. Computation and uses of these decompositions in practice.
CS 676Numeric Computation for Financial Modelling0.50 units
The interaction of financial modes, numerical methods, and computing environments. Basic computational aspects of option pricing and hedging. Numerical methods for stochastic differential equations, strong and weak convergence. Generating correlated random numbers. Time-stepping methods. Finite difference methods for Black-Scholes equation. Discretization, stability, convergence. Methods for portfolio optimization, effect of data errors on portfolio weights. (Heldwith CS 476).
An introduction to neural network methods, with some discussion of their relevance to neuroscience. Simple neuron models and networks of neurons. Training feedforward networks for classification or regression. Learning using the backpropagation of errors. Unsupervised learning methods. Optimal linear decoding. Recurrent neural networks. Convolutional neural networks. Advanced topics, including adversarial inputs and biologically plausible learning methods.
Introduction to modeling and algorithmic techniques for machines to learn concepts from data. Generalization: underfitting, overfitting, cross-validation. Tasks: classification, regression, clustering. Optimization-based learning: loss minimization, regularization. Statistical learning: maximum likelihood, Bayesian learning. Algorithms: nearest neighbor, (generalized) linear regression, mixtures of Gaussians, Gaussian processes, kernel methods, support vector machines, deep learning, sequence learning, ensemble techniques. Large scale learning: distributed learning and stream learning. Applications: Natural language processing, computer vision, data mining, human computer interaction, information retrieval.
CS 682Computational Techniques in Biological Sequence Analysis0.50 units
Computer science principles and algortihms in biological sequence analysis. Topics include algotithms for sequence comparison, for large-scale database search in biological databases, for sequence assembly, for evolutionary tree reconstruction, for identifying important featrues in DNA and RNA sequences, and underlying computational techniques for understanding strings and trees and for making probabilistic inferences. (Heldwith CS 482).
Introduction to image and vision understanding by computer. Camera-system geometry, image formation and lighting, and image acquisition. Basic visual processes for recognition of edges, regions, lines, and surfaces. Processing of stereo images, and motion in image sequences. Object recognition. Applications of computer vision systems.
CS 685Machine Learning: Statistical and Computational Foundations0.50 units
Extracting meaningful patterns from random samples of large data sets. Statistical analysis of the resulting problems. Common algorithm paradigms for such tasks. Central concepts: VC-dimension, Margins of classifier, Sparsity and description length. Performance guarantees: Generalization bounds, data dependent error bounds and computational complexity of learning algorithms. Common paradigms: Neural networks, Kernel methods and Support Vector machines, Applications to Data Mining.
CS 686Introduction to Artificial Intelligence0.50 units
Goals and methods of artificial intelligence. Methods of general problem solving. Introduction to mathematical logic Mechanical theorem proving. Game playing. Natural language processing. Preference will be given to CS graduate students. All others require approval from the department.
CS 687Introduction to Symbolic Computation0.50 units
An introduction to the use of computers for symbolic mathematical computation, involving traditional mathematical computations such as solving linear equations (exactly), analytic differentiation and integration of functions, and analytic solution of differential equations.
Software and hardware for interactive computer graphics. Implementation of device drivers, 3-D transformations, clipping, perspective, and input routines. Data structures, hidden surface removal, colour shading techniques, and some additional topics will be covered. Preference will be given to CS graduate students. All others require approval from the department.
This is an individual study course carried out under the supervision of a Computer Science faculty member. The topic should be agreed upon by both the student and the instructor. This is a credit/no credit course. Department permission will be by Coordinator of Graduate Studies.
This is an individual study course carried out under the supervision of a Computer Science faculty member. The topic should be agreed upon by both the student and the instructor. This is a grade course. Department permission will be by Coordinator of Graduate Studies.
CS 692The Social Implications of Computing0.50 units
This course is designed to consider the problems encountered by individuals, organizations and society as computer technology is adopted, with a view towards assessing possible courses of action.
Research and life skills that can help graduate students improve their academic and interpersonal competence-research methodologies, library research skills, creative and critical thinking, time management, stress management, technical reading skills, listening skills, oral communication, writing and publishing in computer science, jobs in academica versus industry.
This number is used for courses being offered on a temporary basis. Such a course may be available only once, for example to take advantage of a visiting professor's expertise, or may be offered experimentally until it is determined whether of not the course should become part of the regular course offerings. It may also be used for an individual study course carried out under the supervision of a Computer Science faculty member with the approval from the Associate Chair, Graduate Studies. This is a grade course. Preference will be given to CS graduate students. All others require approval of the Department.
Introduction to data engineering issues in data science. Data management technology objectives. Relational database technology, relational algebra, SQL, transactions, data modelling methodology, entity-relationship models. NoSQL databases including key-value stores, document databases, wide-column stores, graph databases. Overview of big data processing platforms. Data integration including data warehousing, data lakes, ETL and ELT approaches. Data preparation for analysis, data quality, data cleaning. Introduction to several current topics in database research, such as data mining, managing data streams, distributed/parallel databases, HTAP architectures. Open to Master of Data Science and Artificial Intelligence (MDSAI) students as well as Master of Mathematics (MMath) in Data Science students.
Project-oriented course that covers the implementation of relational database management systems. Topics include database system architecture; managing primary and secondary storage; query processing; metadata and catalog management; language processing; query optimization and plan generation; concurrency; failures and recovery; extensibility; client-server interactions.
Management of non-relational databases, such as multimedia databases, text databases, temporal databases or spatial databases. Each offering will target a specific type of data. Topics include rationale for and common applications of non-relational database management; systems and standards; the abstract data model; data definition and manipulation languages; data storage and indexing; query processing and optimization; updates and transaction management.
CS 742Parallel and Distributed Database Systems0.50 units
Algorithms and architectures used in parallel database management systems, with a focus on relational systems. Topics include system architectures; parallel and distributed query processing; federated dtabase systems; distributed transactions; data replication.
CS 743Principles of Database Management and Use0.50 units
An overview of relational databases and how they are used; exposure to relational database technology. Fundamentals of transactions. Database views. Introductions to two or three alternative data models and systems, such as those used for structured text, spatial data, multimedia data, and information retrieval. Introduction to several current topics in database research, such as warehousing, data mining, managing data streams, data cleaning, data integration, and distributed databases.
Project-oriented course that covers optimizing compilers and the implementation of advanced programming language features. Topics include intermediate representations; data-flow, dependence, and alias analysis; optimizing transforms, register allocation, instruction scheduling; memory management, garbage collection, threads, concurrency; single and multiple inheritance, generics, templates, type inference.
Application of formal methods to the verification of computer-based systems. Algebraic and automata preliminaries. Temporal logic and model checking. Decision procedures. Mechanized theorem proving. Advanced topics chosen by the instructor.
A project-oriented course that covers the concepts in software architecture. Topics include basic concepts (components and connectors, rationale, views, architectural diagrams, team implictions, evolution, size considerations), theory underlying softeware architecture, extraction of architecture from implementation artifacts, architecture of web-based and enterprise systems, clustering of subsystems, visualization approaches to software architecture, comprehension and cognitive aspects of software architecture.
CS 747Software Verification Using Proof Assistants0.50 units
Dependent types and the Curry-Howard correspondence (proofs as programs); constructing inductive datatypes and proofs by induction; relations; equality; logical connectives and quantifiers; decision procedures; the simply-typed lambda calculus and theorems about it; intrinsically-typed representation; verification of elementary algorithms.
This course provides a broad discussion of advanced topics in modern distributed systems. The course will discuss the state of the art of these systems and how and why they reached there. The course will discuss current data center hardware and software architecture, communication middleware, and how modern systems can tolerate failures, maintain data consistency, scale, leverage the new hardware capabilities, and exploit highly concurrent hardware.
Antirequisites
Complete all of the following
Not completed nor concurrently enrolled in any of the following:
CS 755System and Network Architectures and Implementation0.50 units
Distributed, multi-user applications are designed and implemented using many underlying technologies that must be coordinated to provide important features such as robustness, scalability, manageability, ubiquitous access, privacy, security, authentication, and role-based access control, to name only a few. The network supporting the application may be crucial to its successful implementation. The application logic itself is likely implemented in a number of languages and programming environments. Students will be provided with an advanced overview of current networking and distributed systems topics, and will apply it to case studies drawn form consumer internet applications, enterprise systems, and medical and healthcare systems.
Antirequisites
Complete all of the following
Not completed nor concurrently enrolled in any of the following:
Cryptographic protocols and their application to secure communication, especially in a network setting. Identification and entity authentication; protocols for key establishment, transport, agreement and maintenance; secret sharing, broadcast encryption, tracing schemes; certificates, public-key infrastructure, PGP.
Introduction to the design and analysis of algorithms that make use of randomization. Topics include review of basic probabiloity and introduction to randomized algorithms; game theoretic techniques; uses of Markov and Chebyshev inequalities; tail inequalities; Markov chains and random walks; algebraic techniques; data structures and graph algorithms.
Further exposure to the design, analysis and application of algorithms for problems defined on graphs. Topics include planarity testing and embedding; classes of planar graphs and fast algorithms for them; trees and tree-like graphs (bounded pathwidth and treewidth); perfect graphs and intersection graphs.
Introduction to the design, analysis and application of algorithms for geometric problems. Topics include convex hull algorithms in two and three dimensions; Voronoi diagrams, Delaunay triangulations, and their applications; linear programming in low dimensions; line segments, planar subdivision, and polygons; range searching.
Further exposure to the classification of problems based on their computational requirements and to mathematical tools designed to explore the structural consequences of such classifications. Topics include relativization, alternation, provably intractable problems, feasible parallel computation; fixed-parameter tractability and the W-hierarchy; Kolmogorov complexity, including algorithmic and algorithmic prefix complexity and their applications.
Antirequisites
Not completed nor concurrently enrolled in any of the following:
Fundamental problems of elementary and algebraic number theory from an algorithmic and computational complexity point of view with emphasis placed on analysis of algorithms. Topics include basic arithmetic algorithms; computation over finite fields; primality testing; algorithms for integer factorization; algorithms in number fields.
Fundamentals of quantum information theory including states, measurements, operations, and their representations as matrices; measures of distance between quantum states and operations; quantum Shannon theory including von Neumann entropy, quantum noiseless coding, strong subadditivity of von Neuman entropy, Holevo's Theorem, and capabilities of quantum channels; theory of entanglement including measures of entanglement, entanglement transformation, and classifications of mixed-state entanglement; other topics in quantum information as time permits.
CS 767Advanced Logic for Computer Science0.50 units
The course provides extended background in mathematical logic and its applications to various branches of computer science. It covers some fundamental concepts such as soundness and completeness theorems, compactness, the expressive power of a logic and the computational complexity of its basic decision problems. These concepts are being demonstrated on first order logic and modal logics. Finally the course discusses examples of applications of these concepts and tools to formal reasoning about programs and about hardware, to data bases, and to knowledge representation.
Review of basics of quantum information and computational complexity; Simple quantum algorithms; Quantum Fourier transform and Shor factoring algorithm: Amplitude amplification, Grover search algorithm and its optimality; Completely positive trace-preserving maps and Kraus representation; Non-locality and communication complexity; Physical realizations of quantum computation: requirements and examples; Quantum error-correction, including CSS codes, and elements of fault-tolerant computation; Quantum cryptography; Security proofs of quantum key distribution protocols; Quantum proof systems. Familiarity with theoretical computer science or quantum mechanics will also be an asset, though most students will not be familiar with both.
Introduction to basic algorithms and techniques for numerical computing. Error analysis, interpolation (including splines), numerical differentiation and integration, numerical linear algebra (including methods for linear systems, eigenvalue problems, and the singular value decomposition), root finding for nonlinear equations and systems, numerical ordinary differential equations, and approximation methods (including least squares, orthogonal polynomials, and Fourier transforms).
Option pricing, hedging, model calibration, and portfolio optimization will be discussed. Computational methods, including PDE methods, Monte Carlo, and mathematical programming will be presented.
CS 775Parallel Algorithm in Scientific Computing0.50 units
Techniques for obtaining maximum parallelism in various numberical algorithms, especially those occurring when solving matrix problems and partial differential equations, and the subsequent mapping onto the computer. Topics include: parallel architecture and performance models; message passing/shared memory programming; matrix computations; fast Fourier transform; graph partitioning; domain decomposition methods.
CS 778Numerical Solution of Partial Differential Equations0.50 units
Discretization methods for partial differential equations, including finite difference, finite volume and finite element methods. Application to elliptic, hyperbolic and parabolic equations. Convergence and stability issues, properties of discrete equations, and treatment of non-linearities. Stiffness matrix assembly and use of sparse matric software. Students should have completed a course in numerical computation at the undergraduate level.
CS 779Splines and Their Use in Computer Graphics0.50 units
Spline theory and recent developments in techniques for representing, manipulating and rendering curves and surfaces constructed from splines in a graphic environment. Applications of interest include computer-aided design, synthetic image generation and animation.
Antirequisites
Not completed nor concurrently enrolled in any of the following:
A deep investigation into fundamental problems of symbolic computation. These may include algorithms for linear, non-linear, and differential systems of symbolic equations, symbolic integration, factoring polynomials, and symbolic-numeric algorithms. Covers the basic data types and structures for algebraic objects and operations. Issues in the design of computer algebra systems.
In computer graphics, 3D models are rendered through an ideal camera to a 2D image. The virtual camera is a powerful tool, controlling not only perspective and the optical path, but colour mapping as well. This course examines the notion of "colour" at different stages of the computer graphics pipeline, and describes techniques for modelling and managing colour through the virtual camera analogy. Topics include illumination, the virtual camera, colour spaces, gamut mapping and colour management. Implementation of the colour reproduction and gamut-mapping algorithms demands a number of numerical and statistical methods, including multidimensional interpolation and approximation, computational geometry, splines, principal components anlysis and optimization.
CS 782Pattern Discovery in Biomolecular Data0.50 units
This course examines, from an algorithmic point of view, the pattern discovery techniques that are currently used to extract the functional knowledge hidden in biomolecular data that is derived from DNA, RNA, proteins and their reaction products. Topics include: DNA sequence analysis, RNA structure prediction techniques, protein motif discovery techniques, protein structure prediction, analysis of expression data.
CS 783Computer Modeling of Biophysical Phenomena0.50 units
This course addresses the computer (in silico) simulation of biophysical processes involved in the interaction of light with organic and inorganic matter. Computer models used to simulate these processes are presented, and key stages of the simulation pipeline, such as data gathering, design constraints and evaluation methodologies, are examined in detail. This course also includes discussions of open problems and current trends in the computer simulation of biophysical processes. Application fields include, but are not limited to computer graphics, remote sensing, biology and biomedical optics.
Computer understanding and generation of natural (i.e., human) languages. Basic topics in natural language understanding (syntax, semantics, pragmatics, connected discourse). Selected applications (e.g., automated language generation, machine translation, natural language processing and the Web).
Antirequisites
Not completed nor concurrently enrolled in any of the following:
Intelligence in interfaces-natural language processing, plan recognition, dialogue, generation, user modeling. Interfaces to intelligent systems-intelligent agents and multi-agent systems, information processing and data mining, knowledge-based systems.
Antirequisites
Not completed nor concurrently enrolled in any of the following:
CS685 - Machine Learning: Statistical and Computational Foundations (0.50)
CS 786Probabilistic Inference and Machine Learning0.50 units
Covers the fundamental principles of probabilistic inference and computational learning systems. Topics include Bayes decision and utility theory, Monte Carlo and Markov chain Monte Carlo methods; learning with complete data; Bayesian networks, Markov random fields and factor graphs; models; learning with incomplete data; computational learning and PAC learning theory.
Fundamental problems in computational vision where efficient and robust algorithms can be applied. Topics include image formation; linear systems and Fourier theory; image registration; feature detection; fitting models to data; optical flow; structure from motion; steriopsis; object recognition; high-level vision.
Project-oriented course that covers high-performance image synthesis using techniques for both real-time interactive systems and offline physically-based rendering. Topics include numerical techniques; visual perception and light; mathematical models of rendering; global illumination algorithms; real-time rendering.
Graphics input devices and interaction techniques. The pragmatic factors of various physical, logical and virtual devices, human factors of interactive systems, interaction dialogue managers.
Fundamentals of non-photorealistic computer depiction. Problems of style, abstraction and spatial and temporal coherence. Stroke-based rendering and simulation of traditional artistic media. Stylized processing of images and video. Real-time non-photorealistic rendering of 3D models; toon shaders and contour rendering. Geometric art and ornamental design.
CS 792Data Structures and Standards in Health Informatics0.50 units
This course focuses on health data as a key component of all health informatics systems. Topics include ontologies and other classification taxonomies found in health systems, data standards (with a focus on Canadian implementations of international standards), privacy and security of health data, client/patient assessment tools, and ethical considerations.
Techniques for formulating data science models as optimization problems. Algorithms for solving data science problems with an emphasis on scalability, efficiency and parallelizability including gradient-descent based algorithms, derivative-free algorithms, and randomized algorithms. Theoretical analyses of algorithms and approaches for recognizing the most suitable algorithm for solving a particular problem.