This course provides students with the relevant introduction to machine learning. It examines the field of machine learning with a focus on how to construct computer programs that automatically improve with experience. The topics covered include exponential family distributions, Bayesian networks, Bayesian inference, maximum likelihood, maximum entropy, mixture models, EM algorithm, graphical models, hidden Markov models, variational methods, linear classifiers, regression, generalization bounds, support vector machines, kernel methods and transduction, machine learning applications of detecting fraudulent card transactions, learning users’ reading preferences, and autonomous vehicles that learn to drive.