We
will
review statistical and mathematical
principles that are utilized in the data
mining and machine learning research.
Covered topics include asymptotic analysis
of parameter estimation, sufficient
statistics, model selection, information
geometry, function approximation and
Hilbert spaces.

Prerequisites:

EECS 738, EECS 837,
EECS 844 or equivalent.

Textbook
(not required but recommended):

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