Machine learning is the study of computer
algorithms that improve automatically
through experience" (Tom Mitchell). This
course introduces basic concepts and
algorithms in machine learning. A variety of
topics such as Bayesian decision theory,
dimensionality reduction, clustering, neural
networks, hidden Markov models, combining
multiple learners, reinforcement learning,
Bayesian learning etc. will be covered.

Prerequisites:

Graduate standing in CS or CoE or consent
of the instructor

Text books:

All of Statistics, by Larry
Wasserman, Springer, 2006, ISBN-13:978-0387402727.

The
Elements of Statistical Learning: Data
Mining, Inference, and Prediction, by
Trevor Hastie, Robert Tibshirani, and
Jerome Friedman, 2nd edition, 2009,
Springer, ISBN-13: 978-0387952840.

Pattern
Recognition and Machine Learning, By
Christopher Bishop, Springer,2nd
printing edition, 2007, ISBN-10:
0387310738, ISBN-13: 978-0387310732

Numerical
Optimization, by Jorge Nocedal and
Stephen Wright, Springer; 2nd edition,
2006, ISBN-10: 0387303030, ISBN-13:
978-0387303031

Machine Learning:
A Probabilistic Perspective, Kevin
Murphy, The MIT Press (August 24,
2012), ISBN-10: 0262018020, ISBN-13:
978-0262018029