Machine Learning Lectures

  • Lectures 1: Introduction. PDF

  • Lectures 1: Google, SVD, PCA, maximum likelihood, Bayesian learning, Linear-Gaussian models, cross-validation and Gibbs sampling. PDF

  • Lectures 2: Monte Carlo, importance sampling, Metropolis, back-propagation and neural networks. PDF

  • Lectures 3: Gaussian processes, K-means, mixture models, EM, HMMs, Kalman filtering and particle filtering. PDF