This course covers the issues of ethics, privacy, algorithmic fairness, explainability and transparency of data and algorithms, and the legal and regulatory frameworks for these issues. The course also includes a module on Indigenous principles in data governance. The objectives are to learn how to identify and measure these aspects in outputs of algorithms, and how to build algorithms that correct for these issues. The course introduces these concepts with real case studies, followed by a technical treatment of the topics. Students will learn and implement basic data science and machine learning methods, and tools and techniques for privacy and mitigation of algorithmic unfairness. Prerequisite: one of CMPUT 191 or 195; or one of CMPUT 174 or 274 or ENCMP 100, and one of STAT 151, 161, 181, 235, 265, SCI 151, or MATH 181.
| Section | Capacity | Class times | Login to view Instructor(s) and Location |
|---|---|---|---|
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LECTURE A1
(54963) |
250 |
2026-09-01 - 2026-12-08 (TR)
11:00 - 12:20
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| Section | Capacity | Class times | Login to view Instructor(s) and Location |
|---|---|---|---|
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LAB D01
(54964) |
32 |
2026-09-01 - 2026-12-08 (W)
14:00 - 16:50
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LAB D02
(54965) |
215 |
2026-09-01 - 2026-12-08 (R)
17:00 - 19:50
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| Section | Capacity | Class times | Login to view Instructor(s) and Location |
|---|---|---|---|
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LECTURE B1
(79144) |
214 |
2027-01-04 - 2027-04-09 (TR)
11:00 - 12:20
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| Section | Capacity | Class times | Login to view Instructor(s) and Location |
|---|---|---|---|
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LAB H01
(79145) |
195 |
2027-01-04 - 2027-04-09 (T)
17:00 - 19:50
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LAB H02
(79970) |
24 |
2027-01-04 - 2027-04-09 (F)
14:00 - 16:50
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