Course Preview

Machine Learning
Instructor: Tom Mitchell, Maria-Florina Balcan
Department: Machine Learning
Institution: Carnegie Mellon University
Platform: Independent
Price: Free
Tom Mitchell: "Machine Learning", McGraw Hill, 1997.
Chris Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning. 2009.
Kevin P. Murphy. Machine Learning: A Probabilistic Perspective.
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. 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.