Introduction to Machine Learning
MIT's introduction to machine learning covers principles, algorithms, and applications from the perspective of modeling and prediction. The course formulates what a learning problem actually is, then works through representation, over-fitting, and generalization as recurring themes. Supervised learning and reinforcement learning both get dedicated treatment, with applications to image data and to temporal sequences giving the abstract concepts something concrete to attach to. The course lives in MIT's Open Learning Library, so all lecture materials, problem sets, and exercises are available to view and use without enrolling, and you can optionally create an account to track your progress through the modules. There is no instructor video lecture series bundled in this listing description, but the structured problem sets and concept sequencing make it usable as a self-paced study of core machine learning ideas rather than a single talk or overview.