Geoff Gordon and Alex Smola, professors in the Carnegie Mellon University machine learning department have made available course materials for the Introduction to Machine Learning class open to anyone with a desire to get up to speed with this growing field of technology. The graduate-level material is designed to give PhD students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from machine learning, classical statistics, data mining, Bayesian statistics and information theory. Students are urged to possess a working knowledge of probability, statistics and algorithms, but the class has been designed so that anyone with a strong numerate background can catch up and successfully complete the material.
Course materials include both lecture videos and slides. The first lecture video is included below.