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Machine Learning >> Content Detail



Study Materials



Readings

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LEC #TOPICSREADINGS
1Introduction, linear classification, perceptron update rule
2Perceptron convergence, generalization
3Maximum margin classification

Optional


Amazon logo 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.

4Classification errors, regularization, logistic regression
5Linear regression, estimator bias and variance, active learning
6Active learning (cont.), non-linear predictions, kernals
7Kernal regression, kernels
8Support vector machine (SVM) and kernels, kernel optimization

Short tutorial on Lagrange multipliers (PDF)



Optional


Stephen Boyd's course notes on convex optimization

Amazon logo Boyd, Stephen, and Lieven Vandenberghe. Convex Optimization. Cambridge, UK: Cambridge University Press, 2004. ISBN: 9780521833783.

9Model selection
10Model selection criteria
Midterm
11Description length, feature selection
12Combining classifiers, boosting
13Boosting, 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.

14Margin 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.

15Mixtures and the expectation maximization (EM) algorithm
16EM, regularization, clustering
17Clustering
18Spectral 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.

19Hidden 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.

20HMMs (cont.)
21Bayesian networks

Optional


Amazon logo 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.

22Learning Bayesian networks
23

Probabilistic inference

Guest lecture on collaborative filtering

Final
24Current problems in machine learning, wrap up



References


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

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

Amazon logo 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.

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

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

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


 








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