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10-601 Course Description

Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., that learn to spot high-risk medical patients, recognize speech, classify text documents, detect credit card fraud, or drive autonomous robots).

This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as datamining, decision tree learning, neural network learning, learning of natural language, hidden markov modeling, statistical learning methods, genetic algorithms, Bayesian learning methods, explanation-based learning, and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, minimum description length principle, and Occam's Razor. We will also provide brief tutorials on Information Theory and Bayesian Statistics, as needed.

Short programming assignments include hands-on experiments with various learning algorithms. Typical assignments include neural network learning of DNA splice junctions, and decision tree learning from databases of credit records. Advanced students will be offered the opportunity to engage in current machine learning challenges involving speech, language, and computational biology.

Prerequisite: 15-211 or permission of the instructor. It is also desirable to have taken a college-level introduction to Probability and Statistics.

[This is the same course as was offered as “15-681 Machine Learning” until 2006.]

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