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Course 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.