Mathematics for Machine Learning
Mathematical foundations of machine learning and artificial intelligence, focusing primarily on linear algebra, along with select topics from calculus and probability. Topics include matrices, vectors, projections, spans, least squares, and eigenvalue and eigenvector problems. Includes mathematical theory as well as application to concrete data sets in a modern programming language.
Topics
- matrices
- vectors
- projections
- spans
- least squares
- and eigenvalue and eigenvector problems
Topics explicitly listed in official catalogue description
Prerequisites & course information
[EECS 203 or MATH 116 or 121 or 156 or 186 or 205 or 215 or 216 or 275 or 276 or 285 or 286 or 295 or 296 or 316 or ROB 201; (C or better, no OPF)] and [EECS 180 or 183 or 280 or ENGR 101 or ROB 102 or COMPFOR 131 or SI 206; (C or better, no OPF)]. (Prerequisites enforced at registration.) (BS). May not be repeated for credit.
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- Mathematics for Machine Learning · 4 · College of Engineering catalogue