The course introduces basic concepts and techniques of data analysis and machine learning. Topics include: data preprocessing techniques, decision trees, nearest neighbor algorithms, linear and logistic regressions, clustering, dimensionality reduction, model evaluation, deployment methods, and emerging topics. Prerequisites: ECE 220 or CMPUT 275, and ECE 342 or STAT 235, or consent of instructor.
| Section | Capacity | Class times | Instructor(s) |
|---|---|---|---|
|
LECTURE B1
(18367) |
127 |
2024-01-08 - 2024-04-12 (TR)
11:00 - 12:20
MEC 2-1
|
Primary Instructor: Marek Reformat
|
| Section | Capacity | Class times | Instructor(s) |
|---|---|---|---|
|
SEMINAR J31
(18368) |
127 |
2024-01-08 - 2024-04-12 (W)
08:00 - 08:50
CSC B-02
|
Primary Instructor: Marek Reformat
|
| Section | Capacity | Class times | Instructor(s) |
|---|---|---|---|
|
LECTURE B1
(76560) |
150 |
2025-01-06 - 2025-04-09 (TR)
11:00 - 12:20
MEC 2-1
|
|
| Section | Capacity | Class times | Instructor(s) |
|---|---|---|---|
|
SEMINAR J31
(76561) |
150 |
2025-01-06 - 2025-04-09 (W)
08:00 - 08:50
CCIS L1-160
|
|