CMPUT 267: Machine Learning I

Course description

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.

Course notes and materials

There is no required textbook. The course content is based almost entirely on the course notes, which were made specifically for this course. The notes are designed to be short so that you can read every chapter.

Additional useful materials

The following optional textbooks may be useful as supplementary references:

• Machine Learning Specialization on Coursera — highly recommended. It covers additional topics, including unsupervised learning, and provides a more detailed introduction to basic neural networks.
• Deep Learning Foundations and Concepts, by Christopher Bishop
• Mathematics for Machine Learning, by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
• Understanding Machine Learning, by Shai Shalev-Shwartz and Shai Ben-David (a more theory-heavy book)

Teaching assistants

TA office hours begin September 7.

Assignment schedule

Recommended completion data: completing an assignment by the recommended date will help you consolidate your knowledge and prepare for progress checks and exams. Hard deadline: The due date shown on Canvas. Assignments submitted after the hard deadline will not be accepted.

Schedule

Lectures meet Tuesdays and Thursdays. Slides will be posted after each lecture. Readings refer to the course notes.