Fall 2003
Fall 2002
Fall 2001

References

Optional papers (also listed with lectures):

• C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining, 2(2), 1998. postscript

• R. Schapire, "A brief introduction to boosting", In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999. postscript

• J. Friedman, T. Hastie, and R. Tibshirani, "Additive Logistic Regression: a Statistical View of Boosting", postscript

• Jordan, "Introduction to Probabilistic Graphical Models", draft version available electronically here (MIT only access)

• R. Duda, P. Hart, and D. Stork. "Pattern Classification", 2nd edition, Wiley Interscience, 2001.

• C. M. Bishop. "Neural Networks for Pattern Recognition", Oxford University Press, 1995.

• T. Mitchell, "Machine Learning". McGraw Hill, 1997.

• T. Hastie, R. Tibshirani and J. Friedman, "Elements of Statistical Learning: Data Mining, Inference and Prediction". Springer-Verlag, 2001.

• T. Cover and J. Thomas. "Elements of Information theory", Wiley Interscience, 1991.