References:

1. MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
2. UBC CPSC 540: Machine Learning by Nando de Freitas
3. Mathematics for Machine Learning by Cambridge University Press
4. Stanford CS228: Probabilistic Graphical Models

Linear Algebra

Positive Definite and Semidefinite Matrices (WIP)

Probability and Statistics

Linear Regression and Regularization

Maximum Likelihood Estimation, Kullback-Leibler Divergence and Entropy

Bayes Theorem (Primer)

Bayesian Learning

Probabilistic Graphical Models