Pattern Recognition Machine Learning

This course introduces the fundamental concepts and methods in pattern recognition and machine learning. Topics covered include Introduction, Bayesian Inference, Linear Models, Mixture Models and EM Algorithm, Markov Models and Hidden Markov Models, Sampling, Markov chain Monte Carlo (MCMC), Neural Networks, Deep Learning (CNN, RNN), Kernel Methods, Applications, Decision Trees and Ensemble Learning, Model Selection and Feature Selection, and Clustering.