CMPUT 466 - Machine Learning Essentials

Course Career Undergraduate
Units 3
Approved Hours 3-0-3
Fee index 6
Faculty Science
Department Computing Science
Typically Offered either term

Description
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.