Here is a great learning resource for anyone wishing to dive into the field of machine learning – a complete class “Machine Learning” from Spring 2011 at Carnegie Mellon University. The course is taught by Tom Mitchell, Chair of the Machine Learning Department. The class materials include: video lectures, slides, supplemental reading materials, handouts (homework assignments and solutions), slides from recitation sections, project materials, and data sets.
The course description is as follows:
Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam’s Razor. Short programming assignments include hands-on experiments with various learning algorithms, and a larger course project gives students a chance to dig into an area of their choice. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.”
If you’d like to get an orderly jump-start into machine learning, these course materials can provide an excellent gateway. To access the course website click HERE.