# Pastebin AQI8FtnT
- Revision of probability theory 
- Bayes theorem, likelihoods and priors 
- Principles of Bayesian inference 
- Maximum likelihood, MAP and Bayesian approaches to Machine Learning 
- Linear Regression and Logistic Regression 
- Regularization 
- Entropy and information 
- Probabilistic graphical models 
- Manifold learning from Gaussian models - MDS PCA and LDA 
- Kernels 
- Naive Bayes models 
- Gaussian models 
- Mixtures of Gaussians 
- (Hidden) Markov models 
- Markov chains 
- The EM algorithm 
- Markov chain Monte Carlo 
- Classification 
- Clustering