Course Timetable

Analysis and simulation, laboratory data acquisition and processing, measurement interfaces, engineering tools, computer systems organization, programming languages. Credit will only be given for one of: APSC_V 160, CPSC_V 301, or EOSC_V 211. [3-2-0].

Examination and comparison of the research methodologies of different disciplines relevant to cognitive systems. [3-0-0] Prerequisite: COGS 200.

Meaning and impact of computational thinking. Solving problems using computational thinking, testing, debugging. How computers work. No prior computing experience required. Not for students with existing credit for or exemption from CPSC 107, CPSC 110 or APSC 160. [3-1-0]

Computation as a tool for systematic problem solving in non-computer-science disciplines. Introductory programming skills. Not for credit for students who have credit for, or exemption from, or are concurrently taking CPSC 110 or APSC 160. No programming experience expected. [3-0-1]

Fundamental computation and program structures. Continuing systematic program design from CPSC 103. [3-2-0] Prerequisite: CPSC 103.

Fundamental program and computation structures. Introductory programming skills. Computation as a tool for information processing, simulation and modelling, and interacting with the world. [3-3-0]

Physical and mathematical structures of computation. Boolean algebra and combinations logic circuits; proof techniques; functions and sequential circuits; sets and relations; finite state machines; sequential instruction execution. [3-2-1] Prerequisite: Principles of Mathematics 12 or Pre-calculus 12. Corequisite: One of CPSC 107, CPSC 110.

Analysis of increasingly complex algorithmic problems, using a modern programming language and a variety of approaches. Problem decomposition and abstraction guide explorations of topics from applied algorithms, for example Voronoi Diagrams, Markov Chains, Bin Packing, and Graph Search. Not for students with credit for, or currently enrolled in, CPSC 210 or CPEN 221. [3-0-1] Prerequisite: One of CPSC 103, CPSC 110, APSC 160, EOSC 211, MATH 210, PHYS 210, COMM 337.

Design, development, and analysis of robust software components. Topics such as software design, computational models, data structures, debugging, and testing. [3-2-0] Prerequisite: One of CPSC 107, CPSC 110.

Software architecture, operating systems, and I/O architectures. Relationships between application software, operating systems, and computing hardware; critical sections, deadlock avoidance, and performance; principles and operation of disks and networks. [3-3-0] Prerequisite: All of CPSC 121, CPSC 210.

Design and analysis of basic algorithms and data structures; algorithm analysis methods, searching and sorting algorithms, basic data structures, graphs and concurrency. [3-2-0] Prerequisite: One of CPSC 210, CPEN 221 and either (a) one of CPSC 121, MATH 220 or (b) a score of 68% or higher in MATH 226.

Advanced procedural programming. Fundamental algorithms for sorting and searching. Data structures including lists, trees, and hash tables. Introduction to scripting languages and file input/output. [3-2-0] Prerequisite: APSC 160.

Numerical techniques for basic mathematical processes involving no discretization, and their analysis. Solution of linear systems, including analysis of round-off errors; norms and condition number; introduction to iterative techniques in linear algebra, including eigenvalue problems; solution to nonlinear equations. [3-0-0] Prerequisite: One of CPSC 103, CPSC 110, CPEN 221, EOSC 211, PHYS 210 and one of MATH 101, MATH 103, MATH 105, MATH 121, SCIE 001 and one of MATH 152, MATH 221, MATH 223.

Numerical techniques for basic mathematical processes involving discretization, and their analysis. Interpolation and approximation, including splines and least squares data fitting; numerical differentiation and integration; introduction to numerical initial value ordinary differential equations. [3-0-0] Prerequisite: One of CPSC 103, CPSC 110, CPEN 221, EOSC 211, PHYS 210 and one of MATH 101, MATH 103, MATH 105, MATH 121, SCIE 001 and one of MATH 152, MATH 221, MATH 223.

Overview of database systems, ER models, logical database design and normalization, formal relational query languages, SQL and other commercial languages, data warehouses, special topics. [3-0-1] Prerequisite: CPSC_V 221 or DSCI_V 221.

Specification, design, validation, evolution and construction of modern software systems, within the context of socially and professionally relevant domains such as ethics, intellectual property, and information security. [3-2-0] Prerequisite: All of CPSC 213, CPSC 221.

Comparative study of advanced programming language features. Statement types, data types, variable binding, parameter passing mechanisms. Methods for syntactic and semantic description of programming languages. [3-0-1] Prerequisite: CPSC 210.

Instruction sets, pipelining, code optimization, caching, virtual memory management, dynamically linked libraries, exception processing, execution time of programs. [3-0-1] Prerequisite: CPSC_V 213 and either CPSC_V 221 or DSCI_V 221.

Human vision and colour; geometric transformations; algorithms for 2-D and 3-D graphics; hardware and system architectures; shading and lighting; animation. [3-1-0] Prerequisite: CPSC 221 and one of MATH 200, MATH 217, MATH 226, MATH 253 and one of MATH 152, MATH 221, MATH 223.

Computer networking, basic communication protocols, network infrastructure and routing. Common application-level protocols and principles associated with developing distributed applications. [3-0-1] Prerequisite: CPSC_V 213 and either CPSC_V 221 or DSCI_V 221.

The design, implementation, and test of a large software system, using a team approach. [3-0-2] Prerequisite: CPSC 310.

Systematic study of basic concepts and techniques in the design and analysis of algorithms, illustrated from various problem areas. Topics include: models of computation; choice of data structures; graph-theoretic, algebraic, and text processing algorithms. [3-0-1] Prerequisite: All of (a) CPSC_V 221 or DSCI_V 221, (b) at least 3 credits from COMM_V 291, BIOL_V 300, MATH or STAT at 200 level or above.

Problem-solving and planning; state/action models and graph searching. Natural language understanding Computational vision. Applications of artificial intelligence. [3-0-0] Prerequisite: CPSC 221.

Application of machine learning tools, with an emphasis on solving practical problems. Data cleaning, feature extraction, supervised and unsupervised machine learning, reproducible workflows, and communicating results. [3-0-1] Prerequisite: Either (a) one of CPSC_V 203, CPSC_V 210, CPEN_V 221, DSCI_V 221 or (b) MATH_V 210 and one of CPSC_V 107, CPSC_V 110.

Models of algorithms for dimensionality reduction, nonlinear regression, classification, clustering and unsupervised learning; applications to computer graphics, computer games, bio-informatics, information retrieval, e-commerce, databases, computer vision and artificial intelligence. [3-0-1] Prerequisite: All of (a) one of CPSC_V 221, DSCI_V 221 (b) one of MATH_V 152, MATH_V 221, MATH_V 223 (c) one of MATH_V 200, MATH_V 217, MATH_V 226, MATH_V 253, MATH_V 254 (d) one of STAT_V 241, STAT_V 251, ECON_V 325, ECON_V 327, MATH_V 302, STAT_V 302, MATH_V 318.

Basic tools and techniques, teaching a systematic approach to interface design, task analysis, analytic and empirical evaluation methods. [2-2-2] Prerequisite: One of CPSC 210, CPEN 221.

Students will attend a series of research seminars presented by faculty members, produce a thesis proposal, and choose their honours thesis supervisor. Available to Honours students. Majors students with satisfactory standing may also be permitted to enrol. [1-0-0]

Overview of relational and non-relational database systems, role and usage of a database when querying data, data modelling, query languages, and query optimization. [3-0-1]

Prerequisite: One of CPSC_V 203, CPSC_V 210, CPEN_V 221, DSCI_V 221.

Physical database design, indexing, external mergesort, relational query processing and optimization, transaction processing, concurrency control, crash recovery, special topics. [3-0-0] Prerequisite: CPSC 304 and one of CPSC 213, CPSC 261, CPEN 212.

Formulation and analysis of algorithms for continuous and discrete optimization problems; linear, nonlinear, network, dynamic, and integer optimization; large-scale problems; software packages and their implementation; duality theory and sensitivity. [3-0] Prerequisite: One of CPSC 302, CPSC 303, MATH 307.

Specification, design, construction and validation of multi-version software systems. [3-0-0] Prerequisite: Either (a) CPSC 310 or (b) all of CPEN 321, CPEN 331.

A practical introduction to lexical analysis, syntactic analysis, type-checking, code generation and optimization. This will be used to design and implement a compiler for a small language. [3-0-0] Prerequisite: All of CPSC 213, CPSC 221, CPSC 311.

Concepts and design of distributed systems. Communication architecture and models for interprocess communication. Process migration, naming, distributed file systems, fault tolerance, and concurrency control. [3-0-0] Prerequisite: One of CPSC 313, CPEN 331 and one of CPSC 317, ELEC 331.

Algorithms, architectures, and programming paradigms for parallel computation. Shared memory, message passing, and data parallel architectures and programming models. Parallel algorithms including reduce, scan, and sorting networks. Reasoning about the correctness of parallel programs. Performance analysis and measurement for parallel programs. [3-0-0] Prerequisite: CPSC 320 and one of CPSC 261, CPSC 313, CPEN 212, CPEN 411.

The study of advanced topics in the design and analysis of algorithms and associated data structures. Topics include algorithms for graph-theoretic; algebraic and geometric problems; algorithms on nonsequential models; complexity issues; approximation algorithms. [3-0-0] Prerequisite: [CPSC320]

Characterizations of computability (using machines, languages and functions). Universality, equivalence and Church's thesis. Unsolvable problems. Restricted models of computation. Finite automata, grammars and formal languages. [3-0-0] Prerequisite: One of CPSC 320 or MATH 220 or MATH 226.

Principles and techniques underlying the design, implementation and evaluation of intelligent computational systems. Applications of artificial intelligence to natural language understanding, image understanding and computer-based expert and advisor systems. Advanced symbolic programming methodology. [3-0-0] Prerequisite: CPSC 322.

Introduction to the processing and interpretation of images. Image sensing, sampling, and filtering. Algorithms for colour analysis, texture description, stereo imaging, motion interpretation, 3D shape recovery, and recognition. [3-0-0] Prerequisite: All of CPSC 221, MATH 200, MATH 221.

Motion in computer graphics for characters and their environments. Keyframing, inverse kinematics, particle systems, rigid body dynamics, contact and collision, controller-based active motion, motion capture. [3-0-0] Prerequisite: CPSC 314.

Video game programming techniques and technologies, including rendering, animation, interaction, game AI, real-time software development for games; start-to-finish simple video-game design and implementation. [1.5–1.5-1] Prerequisites: CPSC_V 314 and one of MATH_V 200, MATH_V 217, MATH_V 226, MATH_V 253 and one of MATH_V 152, MATH_V 221, MATH_V 223.

Impact of computer technology on society; historical perspectives; social and economic consequences of large-scale information processing systems and automatic control; legal and ethical problems in computer applications. Computers and the individual: machine versus human capabilities, fact and fancy; problematic interface between man and machine. [3-0-0] Prerequisite: 3 credits of Computer Science and at least third-year standing.

Selected topics in a specific area within Computer Science. May be taken more than once for credit with permission of the department. Prerequisite: Third-year standing in a Computer Science or Computer Engineering specialization, and permission of the department.

Selected topics in a specific area within Computer Science. May be taken more than once for credit with permission of the department. Prerequisite: Third-year standing in a Computer Science or Computer Engineering specialization, and permission of the department.

Selected topics in a specific area within Computer Science. May be taken more than once for credit with permission of the department. Prerequisite: Third-year standing in a Computer Science or Computer Engineering specialization, and permission of the department.

Selected topics in a specific area within Computer Science. May be taken more than once for credit with permission of the department. Prerequisite: Third-year standing in a Computer Science or Computer Engineering specialization, and permission of the department.

Selected topics in a specific area within Computer Science. May be taken more than once for credit with permission of the department. Prerequisite: Third-year standing in a Computer Science or Computer Engineering specialization, and permission of the department.

Selected topics in a specific area within Computer Science. May be taken more than once for credit with permission of the department. Prerequisite: Third-year standing in a Computer Science or Computer Engineering specialization, and permission of the department.

Advanced machine learning techniques focusing on probabilistic models. Deep learning and differentiable programming, exponential families and Bayesian inference, probabilistic graphical models and other generative models, Monte Carlo and variational inference methods. [3-0-1] Prerequisite: All of CPSC 320, CPSC 340.