1. Notes on Machine Learning

1.1. Probability Theory

Probability theory basics
Bayesian learning
Gaussian process

1.2. Machine learning in practice

Linear algebra in machine learning
Linear prediction
Regularization & cross-validation
L1 Norm and Lasso
Categorical, Dirichlet distribution & Naive Bayes
Optimization
Logistic regression
Neural network

2. Notes on group theory

Lecture note-I
Lecture note-II
Lecture note-III