NEW: Artificial Intelligence Option
Pending Senate Approval
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Description
The Artificial Intelligence (AI) Option, effective September 2026, is available to students who are admitted to a Computer Science major or honours specialization and complete the required courses. It is currently not available for combined degrees, COGS, or Arts. The AI Option allows students to refine their interests and develop coherent expertise within the discipline of Artificial Intelligence.
For this year, once the option has been approved by the UBC Senate (likely May 2026), students who have completed CPSC 213 and 221 by the end of April and are interested in pursuing the AI option may indicate their intent by completing a webform. All students who complete the webform will be added to the option, as long as it is compatible with their current specialization.
Being added to the option does not guarantee access to the courses needed to graduate with it, although the department will try to have enough seats in these courses subject to room and Faculty availability.
Note that it is not possible to pursue both the Artificial Intelligence option and the Software Engineering option.
Program Requirements
The Artificial Intelligence (AI) Option is available to students admitted to a Computer Science major or honours specialization who expect to have third year class standing by the start of the next Winter Session at the time of application.
Major: Computer Science, Option in Artificial Intelligence
First Year and Second Year
Same as Major Computer Science.
Third and Fourth Years
CPSC_V 310, 313, 320 10
AI_V 240 or CPSC_V 3401 4
AI_V 322 or CPSC_V 322 3
AI_V 360 3
CPSC_V 440 3
CPSC_V 430 or DSCI_V 430 3
MATH_V 302 or STAT_V 302 3
Three of CPSC_V 304, 330, 344, 368, 404,
406, 420, 423, 425, 444, 447, AI_V 422
at least 6 credits of which must be 400 or above 9
Electives2 22
Total Credits 60
Total Credits for Degree 120
1 If a lower-credit course is chosen, an additional elective credit is required.
2 Students are permitted to move elective credits between years. Elective credits together with required courses must fulfill the Faculty of Science’s:
a) Foundational Requirement;
b) Laboratory Science Requirement;
c) Science Breadth Requirement;
d) Science and Arts Requirements;
e) Upper-level Requirement;
f) General Degree Requirements.
Honours: Computer Science, Option in Artificial Intelligence
First and Second Year
Same as in the regular Honours specialization.
Third and Fourth Years
CPSC_V 310, 313, 320 10
AI_V 240 or CPSC_V 3401 4
AI_V 322 or CPSC_V 322 3
AI_V 360 3
CPSC_V 440 3
CPSC_V 430 or DSCI_V 430 3
MATH_V 302 or STAT_V 302 3
Four of CPSC_V 304, 330, 344, 368, 404,
406, 420, 423, 425, 444, 447, AI_V 422
at least 3 credits of which must be 400 or above 12
CPSC_V 3492 0
CPSC_V 4493 6
Electives4 19
Total Credits 66
Total Credits for Degree 132
1 If a lower-credit course is chosen, an additional elective credit is required.
2 Taken in third year.
3 Taken in fourth year. It is recommended that students select a thesis topic in artificial intelligence. Students who have completed a research intensive experience in a computer science field may be allowed to waive this requirement. Examples of research intensive experiences include full time summer internships in a research laboratory or Undergraduate Student Research Awards. If this requirement is waived, the student must take 6 additional credits of CPSC_V courses numbered 400 or higher.
4 Students are permitted to move elective credits between years. Elective credits together with required courses must fulfill the Faculty of Science’s:
a) Foundational Requirement;
b) Laboratory Science Requirement;
c) Science Breadth Requirement;
d) Science and Arts Requirements;
e) Upper-level Requirement;
f) General Degree Requirements.
New AI Course List
Courses offered in Fall 2026
AI_V 100 (3) Introduction to Artificial Intelligence
Core methodological paradigms in AI. Strengths and limitations of modern AI systems. Social impacts of AI technologies: economic, cultural, scientific, environmental, political. Philosophical implications and plausible future trajectories for AI.
The course is available to all students, has no prerequisites, and is offered in both Winter Terms.
AI_V 360 (3) Deep Learning
Advanced machine learning focused on deep learning in practice: mathematical foundations, models for unstructured, sequential, spatial, and graph data, methods for prediction, generative modeling, and reinforcement learning, and hands-on experience designing, optimizing, tuning, and applying deep models. [3-0-1]
Deep learning is a core topic in machine learning and the technology driving modern AI systems. This course provides more in-depth exposure to deep learning, its conceptual and mathematical foundations, and the practical knowledge and toolkit needed for designing, debugging, and deploying models. This combined coverage prepares students for further advanced study in AI and equips them to pursue research and applications in university and industry settings.
Prerequisites: Either CPSC_V 340 or all of (a) one of AI_V 240, CPEN_V 355 and (b) MATH_V 200 and (c) one of STAT_V 251, ECON_V 325, ECON_V 327, MATH_V 302, STAT_V 302, MATH_V 318.
The course is offered in both Winter Terms.
Courses offered in Spring 2027
AI_V 240 (4) Introduction to Machine Learning
Machine learning has emerged as the core principle of modern AI and one of the key ideas of modern computer science. This course will serve as a unified introduction to machine learning for AI and computer science specialists.
Core methodological paradigms in machine learning. Models for supervised prediction and unsupervised analysis. Deep learning techniques using differentiable programming. [3-2-0]
Prerequisite: Both (a) one of CPSC_V 107, CPSC_V 110 and (b) one of MATH_V 111, MATH_V 131, MATH_V 152, MATH_V 221, MATH_V 223.
Current CPSC courses adapted to AI courses:
AI_V 322 (3) Foundations of Artificial Intelligence
This is a new course, adapted from CPSC_V 322. Introduction to representation and reasoning via topics such as Search, problem-solving and planning, logic, probabilistic graphical models, preference models, Markov decision processes and reinforcement learning, as well as multi-agent decision making [3-0-0]
Prerequisites: Either CPSC_V 340 or all of (a) one of AI_V 240, CPSC_V 330 and (b) one of CPSC_V 221, DSCI_V 221 and (c) one of STAT_V 251, ECON_V 325, ECON_V 327, MATH_V 302, STAT_V 302, MATH_V 318.
Equivalency: CPSC_V 322.
AI_V 422 (3) Intelligent Systems
This is a new course, adapted from CPSC_V 422. 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: One of AI_V 322, CPSC_V 322.
Equivalency: CPSC_V 422.
New CPSC course:
CPSC_V 423 (3) Natural Language Processing
With the advent of large language models (LLMs) such as ChatGPT, natural language processing (NLP) became one of the fastest growing sub-areas of AI, with applications in all sectors of our society, including healthcare, business, science and government.
Fundamentals of data-driven natural language processing, including applications (machine translation, summarization, question answering), text representations (word embeddings, language models), and statistical and neural-network methods. [3-0-0]
Prerequisites: Either CPSC_V 340 or both (a) one of AI_V 240, CPSC_V 330 and (b) one of STAT_V 200, STAT_V 251, ECON_V 325, ECON_V 327, MATH_V 302, STAT_V 302, MATH_V 318.
Equivalency: CPSC 436N.