Fall Term 2024 (1890)
CMPUT 466 - Machine Learning Essentials
3 units (fi 6)(EITHER, 3-0-3)
Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course provides a broad overview of topics in machine learning, from foundational methods for regression, classification and dimensionality reduction to more complex modeling with neural networks. It will also provide the formal foundations for understanding when learning is possible and practical. This single course is an alternative to the more in-depth two-course sequence on machine learning with CMPUT 267 and 467. Prerequisites: CMPUT 204 or 275; any 300-level Computing Science course; MATH 125 or 127; one of MATH 115, 118, 136, 146, or 156; and one of STAT 141, 151, 161, 181, 235, 265, SCI 151, or MATH 181. Credit cannot be obtained in CMPUT 466 if credit has already been obtained for CMPUT 467.
LECTURE A1 (49247)
2024-09-03 - 2024-12-09
TR 12:30 - 13:50
CMPUT 499 - Topics in Computing Science
3 units (fi 6)(VAR, VARIABLE)
This topics course is designed for a one on one individual study course between a student and an instructor. Prerequisites are determined by the instructor in the course outline. See Note (3) above.
IND A04 (53506)
2024-09-03 - 2024-12-09
01:00 - 01:00
CMPUT 566 - Topics in Computing Science
3 units (fi 6)(VAR, VARIABLE)
LECTURE A1 (49860)
2024-09-03 - 2024-12-09
TR 12:30 - 13:50
CMPUT 605 - Topics in Computing Science
3 units (fi 6)(EITHER, 3-0-0)
IND A12 (53488)
2024-09-03 - 2024-12-09
01:00 - 01:00
CMPUT 651 - Topics in Artificial Intelligence
3 units (fi 6)(EITHER, 3-0-0)
LECTURE A2 (55189)
2024-09-03 - 2024-12-09
F 14:00 - 16:50
Winter Term 2025 (1900)
CMPUT 466 - Machine Learning Essentials
3 units (fi 6)(EITHER, 3-0-3)
Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course provides a broad overview of topics in machine learning, from foundational methods for regression, classification and dimensionality reduction to more complex modeling with neural networks. It will also provide the formal foundations for understanding when learning is possible and practical. This single course is an alternative to the more in-depth two-course sequence on machine learning with CMPUT 267 and 467. Prerequisites: CMPUT 204 or 275; any 300-level Computing Science course; MATH 125 or 127; one of MATH 115, 118, 136, 146, or 156; and one of STAT 141, 151, 161, 181, 235, 265, SCI 151, or MATH 181. Credit cannot be obtained in CMPUT 466 if credit has already been obtained for CMPUT 467.
LECTURE B1 (70809)
2025-01-06 - 2025-04-09
TR 11:00 - 12:20
CMPUT 566 - Topics in Computing Science
3 units (fi 6)(VAR, VARIABLE)
LECTURE B1 (70807)
2025-01-06 - 2025-04-09
TR 11:00 - 12:20