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2025-2026 Graduate Course Descriptions coming soon!

View 2024-2025 Fall/Winter Graduate Course Descriptions

Graduate Course Descriptions

Updated on December 19, 2024 @ 12:23 PM

Enrolment Notes:

Enrolment for graduate CS students will open on July 25, 2024 at 10:00AM ET.

  •  Enrolment for non-CS graduate students will open on August 22, 2024 at 10:00AM ET; SGS Add/Drop Courses forms are not required for CS graduate course enrolment unless it is required by the student’s home department.

  • Fall graduate courses start on Tuesday, September 3, 2024

  • Winter graduate courses start on Monday, January 6, 2025.

  • Graduate students must enroll in the Graduate Section of cross-listed courses. The Graduate Section is designated by the 4-digit course code. eg: CSC2209H/CSC458H1.

Program Specific Notes:

  • MSc students must complete 4 graduate level half-courses (2.0 FCE) and cover breadth in 2 different groups. Please visit the MSc Handbook for details.

  • PhD students (Transitioned and External Masters) must complete 4 graduate level half-courses (2.0 FCE) and complete breadth in 3 different groups. Please visit the PhD Handbook for details.

  • PhD U students (Direct-Entry) must complete 8 graduate level half-courses (4.0 FCE) these must include courses from at least 3 different groups. Please visit the PhD Handbook for details.

  • MScAC (Computer Science Concentration) students must complete six graduate level half-courses (3.0 FCE) and cover breadth in 2 course groups. No more than 1 course (0.5 FCE) from Group 2 will be counted towards program requirements. A minimum of 2 courses (1.0 FCE) must be from the Computer Science timetable (i.e. CSCXXXX course code) and courses from other departments may fulfill up to 1.0 FCE of course requirements if approved by the MScAC program. Students must complete two required courses (1.0 FCE): CSC2701H and CSC2702H.

  • MScAC (Applied Math Concentration) students must complete six graduate level half-courses (3.0 FCE). Two graduate level half-courses (1.0 FCE) should be selected from two groups in the Department of Computer Science course schedule. Two graduate level half-courses (1.0 FCE) should be selected from the Department of Mathematics. Students must complete two required courses (1.0 FCE): CSC2701H and CSC2702H.

  • MScAC (Artificial Intelligence) students must complete six graduate level half-courses (3.0 FCE). Two graduate level half-courses (1.0 FCE) should be selected from the core list of AI courses. One graduate level half-course (0.5 FCE) should be selected from Group 2. One graduate level half-course (0.5 FCE) should be chosen from Group 1, 3 or 4. Students must complete two required courses (1.0FCE): CSC2701H and CSC2702H.

  • MScAC (Artificial Intelligence in Healthcare) students must complete six graduate level half-courses (3.0 FCE). One graduate level half-course (0.5 FCE) should be selected from approved data science courses. One graduate level half-course (0.5 FCE) should be selected from Group 2. One graduate level half-course should be selected from Group 3 (0.5 FCE). One graduate level half-course (0.5 FCE) should be selected from approved courses in the Faculty of Medicine. Students must complete two required courses (1.0 FCE): CSC2701H and CSC2702H.

  • MScAC (Data Science Concentration) students must complete six graduate level half-courses (3.0 FCE). Two graduate level half-courses (1.0 FCE) should be selected from two groups in the Department of Computer Science course schedule. Two graduate level half-courses (1.0 FCE) should be selected from the Department of Statistics courses at STA2000H or higher. Students must complete two required courses (1.0FCE): CSC2701H and CSC2702H.

  • MScAC (Data Science for Biology Concentration) students must complete six  graduate level half-courses (3.0 FCE). Two graduate level half-courses (1.0 FCE) should be selected from two groups in the Department of Computer Science course schedule. Two graduate level half-courses (1.0 FCE) should be selected from graduate course schedules in CSB, EEB, MMG or Statistics. From these, a maximum of one course (0.5 FCE) may be selected from EEB, MMG and Statistics. Students must complete two required courses (1.0FCE): CSC2701H and CSC2702H.

  • MScAC (Quantum Computing Concentration) students must complete six graduate level half-courses (3.0 FCE). Two graduate level half-courses (1.0 FCE) should be selected from two groups in the Department of Computer Science course schedule. Two graduate level half-courses (1.0 FCE) should be selected from the Department of Physics course schedule. Students must complete two required courses (1.0FCE): CSC2701H and CSC2702H.

Graduate courses in the Department of Computer Science are divided into 4 groups, depending on their subject area:

Group 1: Algorithms, Complexity, Cryptography, Theory of Distributed Computing

Group 2: Artificial Intelligence, Machine Learning, Knowledge Representation, Computational Linguistics, Computational Biology and Medicine, Robotics, Vision

Group 3: Systems, Networks, Databases, Security, Programming Languages, Compilers, Software Engineering, Scientific Computing

Group 4: Human Computer Interaction, Computational Social Science, Visualization, Graphics, Sustainability Computing, Computer Science Education

The course descriptions below are available for the 2024-2025 academic year. For a full list of gradute courses at the Department of Computer Science please visit the SGS Calendar entry for Computer Science.

Group 4: Human Computer Interaction, Computational Social Science, Visualization, Graphics, Sustainability Computing, Computer Science Education

CSC2558H — Topics in Multidisciplinary HCI: Qualitative Data Analysis and Writing for HCI

This summer seminar is designed for graduate students in Human-Computer Interaction (HCI) who aim to enhance their qualitative research and academic writing skills. Qualitative research methods are common, though often poorly understood, in HCI. Over the term, students will gain an understanding of qualitative data analysis methods, learn to craft polished research papers, and work towards preparing their own manuscripts for publication.

This is a project-based course, with students expected to bring a qualitative dataset of their own to analyze and work with during class, with the goal of producing a paper that is submittable to the September deadline of the ACM SIGCHI Conference on Human-Computer Interaction (CHI). For those without existing data, guidance and resources will be provided to help identify suitable datasets.

Early sessions will cover the philosophical foundations of qualitative research, data collection strategies, and methodological approaches such as ethnography, grounded theory, and thematic analysis. As the course progresses, students will focus on coding data, developing themes, and drafting each section of a research paper. Emphasis will also be placed on developing effective writing habits, self-editing techniques, as well as providing and responding to peer review. The course culminates in in-class presentations of student papers and the submission of a polished manuscript.