CMPUT 267
Machine Learning I
Winter 2024 · Basics of Machine Learning
Topics
- Univariate probability
- Concentration inequalities
- Maximum-likelihood, MAP and Bayesian estimation
- Gradient descent
- Linear, polynomial and Bayesian regression
- Logistic classification
- Regularization and generalization
- Performance evaluation
Readings
- The Machine Learning Notes.
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An Introduction to Statistical Machine Learning (title as listed on the course site).
- T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning.
- David Barber. Bayesian Reasoning and Machine Learning.
- C. M. Bishop. Pattern Recognition and Machine Learning.
- Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning.
Sources
- Course catalogue ↗2026–2027
- Basics of Machine Learning ↗2024