Pattern Recognition & Deep Learning

Description

This course introduces the fundamental concepts and methods in pattern recognition and machine learning. Topics covered include Introduction, Bayesian Inference, Mixture Models and EM Algorithm, Markov Models and Hidden Markov Models, Sampling, Markov chain Monte Carlo (MCMC), Neural Networks, Deep Learning (CNN, RNN), Training Deep Networks, Deep Network Architectures, Applications, Generative Models and Self-Supervised Learning.