MATH 308 — Fundamentals of Statistical Learning

Name: MATH 308 — Fundamentals of Statistical Learning
Rating: 2.73 (135 reviews)

Theory and application of various techniques for the exploration and analysis of multivariate data: principal component analysis, correspondence analysis, and other visualization and dimensionality reduction techniques; supervised and unsupervised learning; linear discriminant analysis, and clustering techniques. Data applications using appropriate software.

Rating: 2.73 out of 5 from 135 student reviews
Difficulty: 2.30 out of 5
Credits: 3
Faculty: Faculty of Science
Department: Mathematics and Statistics
Prerequisite(s): MATH 208, one of MATH 223, MATH 236, MATH 247, MATH 251; MATH 323 or MATH 356.