CMPUT 267 - Basics of Machine Learning

★ 3 (fi 6)(EITHER, 3-0-0)

Faculty of Science

This course introduces the fundamental statistical, mathematical, and computational concepts in analyzing data. The goal for this introductory course is to provide a solid foundation in the mathematics of machine learning, in preparation for more advanced machine learning concepts. The course focuses on univariate models, to simplify some of the mathematics and emphasize some of the underlying concepts in machine learning, including: how should one think about data, how can data be summarized, how models can be estimated from data, what sound estimation principles look like, how generalization is achieved, and how to evaluate the performance of learned models. Prerequisites: CMPUT 174 or 274; one of MATH 100, 114, 117, 134, 144, or 154. Corequisites: CMPUT 175 or 275; CMPUT 272; MATH 125 or 127; one of STAT 141, 151, 235, or 265, or SCI 151.

No syllabi

Fall Term 2023

Lectures

Section Capacity Class times Instructor(s)
LECTURE A1
(85381)
213
2023-09-05 - 2023-12-08 (TR)
12:30 - 13:50
ETLC E1-013

Final Exam:
2023-12-12
09:00 - 12:00
ETLC E1-013
Primary Instructor: X Li

Winter Term 2024

Lectures

Section Capacity Class times Instructor(s)
LECTURE B1
(15656)
242
2024-01-08 - 2024-04-12 (TR)
14:00 - 15:20
TEL 150
Primary Instructor: Nidhi Hegde