About
Table of contents
Class Hours
Lecture: Tuesdays and Thursdays, 9:45 AM - 11:00 AM - Keller Hall 3-230. Some activities will be semi-synchronous.
Seating is limited by available staff support, which has yet to be determined, as well as room size.
Textbook
Introduction to Machine Learning; by Ethem Alpaydin (3rd ed, 2014, or 4th ed, 2020?).
Access via EBSCOHost
(Requires UofM login or campus VPN.)
Instructor
Prof. Yoga Varatharajah
Office: Keller 4-203
Office Hours: TBD
General Information
Neural networks, non-parametric windowing, and Bayes statistical theory are three popular methods for recognizing and classifying patterns - the process of Pattern Recognition. These are the basic machine learning algorithms applicable to high-dimensional numerical data. We introduce the fundamental concepts of these various approaches, including the classification phase and the learning phase. Part of the class will be devoted to methods for unsupervised learning and classification. We assume just some knowledge of elementary statistics, calculus, and elementary linear algebra at the upper division undergraduate level, plus programming experience in python. A combination of written assignments and programming projects will be used to illustrate the concepts. All programming will be done in python.