Introduction to Machine Learning

6.036 Introduction to Machine Learning is MIT's undergraduate machine learning course, offered in the Open Learning Library as a free, self-paced course with interactive exercises. It introduces the principles, algorithms and applications of machine learning from the point of view of modelling and prediction, including how learning problems are formulated and the concepts of representation, over-fitting and generalisation. These ideas are exercised through supervised learning and reinforcement learning, with applications to images and to temporal sequences. The course includes lectures, lecture notes, exercises, labs and homework problems, and the Open Learning Library platform lets you enter answers and receive instant feedback and track your own progress if you create a free account; you can also browse the material anonymously.

Recommended prerequisites are Python programming, calculus and linear algebra, and the course lists Leslie Kaelbling, Tomás Lozano-Pérez, Isaac Chuang and Duane Boning as instructors. MIT estimates 13 weeks at 12 hours per week. There are no fees anywhere in the Open Learning Library and certificates cannot be earned there; the page states this explicitly. Unlike OpenCourseWare content, the 6.036 material is marked all rights reserved by the instructors rather than Creative Commons, so it is for personal study rather than reuse.