15-681 Artificial Intelligence: Machine Learning

Units: 12.0 Department: Computer Science Prerequisites: 15-211 Notes: In addition to the 15-211 prerequisite, it is very desirable to have taken at l. ast an intro-level course in probability and statistics . Related URLs: http://www.csd.cs.cmu.edu Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to spot high-risk medical patients, recognize human faces, detect credit card fraud, 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 datamining, decision tree learning, neural network learning, 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. Short programming assignments include hands-on experiments with various learning algorithms. Typical assignments include neural network learning for face recognition, and decision tree learning from databases of credit records.

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Spring 2005 times

No sections available for semester Spring 2005.