MATH 400 Introduction to Machine Learning




This course introduces a variety of supervised and unsupervised machine learning algorithms. Students will study methods such as regression and classification, decision trees, naive Bayes, Principal Component Analysis, support vector machines, neural networks, and unsupervised learning methods. A brief introduction to Deep Learning will also be included. Students will gain hands-on experience applying machine learning techniques to real-world data from multiple disciplines such as social sciences, life sciences, physical sciences, economics, education, and engineering.


STAT 305; and MATH 116 or MATH 108

Course Notes

MATH 400 is an approved Numeracy course for Cap Core requirements. MATH 400 is an approved Science and Technology course for Cap Core requirements.

MATH 400 is an approved Science course. MATH 400 is an approved Quantitative/Analytical course for baccalaureate degrees.

Course Outlines

Please note: Course outlines of record posted may vary from the section syllabus distributed by each instructor (e.g. textbooks, assignments, timing of midterms).

Effective Term
PDF Acrobat Adobe Reader Fall 2020 onwards


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