Machine Learning

Readings

LEC #	TOPICS	READINGS
1	Introduction, linear classification, perceptron update rule
2	Perceptron convergence, generalization
3	Maximum margin classification	Optional Cristianini, Nello, and John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge, UK: Cambridge University Press, 2000. ISBN: 9780521780193. Burges, Christopher. “A Tutorial on Support Vector Machines for Pattern Recognition.” Data Mining and Knowledge Discovery 2, no. 2 (June 1998): 121-167.
4	Classification errors, regularization, logistic regression
5	Linear regression, estimator bias and variance, active learning
6	Active learning (cont.), non-linear predictions, kernals
7	Kernal regression, kernels
8	Support vector machine (SVM) and kernels, kernel optimization	Short tutorial on Lagrange multipliers (PDF) Optional Stephen Boyd’s course notes on convex optimization Boyd, Stephen, and Lieven Vandenberghe. Convex Optimization. Cambridge, UK: Cambridge University Press, 2004. ISBN: 9780521833783.
9	Model selection
10	Model selection criteria
	Midterm
11	Description length, feature selection
12	Combining classifiers, boosting
13	Boosting, margin, and complexity	Optional Schapire, Robert. “A Brief Introduction to Boosting.” Proceedings of the 16th International Joint Conference on Artificial Intelligence, 1999, pp. 1401-1406.
14	Margin and generalization, mixture models	Optional Bartlett, Peter, Yoav Freund, Wee sun Lee, and Robert E. Schapire. “Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods.” Annals of Statistics 26, no. 5 (1998): 1651-1686.
15	Mixtures and the expectation maximization (EM) algorithm
16	EM, regularization, clustering
17	Clustering
18	Spectral clustering, Markov models	Optional Shi, Jianbo, and Jitendra Malik. “Normalized Cuts and Image Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 22, no. 8 (2000): 888-905.
19	Hidden Markov models (HMMs)	Optional Rabiner, Lawrence R. “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.” Proceedings of the IEEE 77, no. 2 (1989): 257-286.
20	HMMs (cont.)
21	Bayesian networks	Optional Heckerman, David. “A Tutorial on Learning with Bayesian Networks.” In Learning in Graphical Models by Michael I. Jordan. Cambridge, MA: MIT Press, 1998. ISBN: 9780262600323.
22	Learning Bayesian networks
23	Probabilistic inference Guest lecture on collaborative filtering
	Final
24	Current problems in machine learning, wrap up

References

Bishop, Christopher. Neural Networks for Pattern Recognition. New York, NY: Oxford University Press, 1995. ISBN: 9780198538646.

Duda, Richard, Peter Hart, and David Stork. Pattern Classification. 2nd ed. New York, NY: Wiley-Interscience, 2000. ISBN: 9780471056690.

Hastie, T., R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York, NY: Springer, 2001. ISBN: 9780387952840.

MacKay, David. Information Theory, Inference, and Learning Algorithms. Cambridge, UK: Cambridge University Press, 2003. ISBN: 9780521642989. Available on-line here.

Mitchell, Tom. Machine Learning. New York, NY: McGraw-Hill, 1997. ISBN: 9780070428072.

Cover, Thomas M., and Joy A. Thomas. Elements of Information Theory. New York, NY: Wiley-Interscience, 1991. ISBN: 9780471062592.