6.867 | Fall 2006 | Graduate

Machine Learning


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


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)


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  
11 Description length, feature selection  
12 Combining classifiers, boosting  
13 Boosting, margin, and complexity


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


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


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)


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


Buy at MIT Press 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  

Probabilistic inference

Guest lecture on collaborative filtering

24 Current problems in machine learning, wrap up  


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.

Course Info

Learning Resource Types
Problem Sets with Solutions
Exams with Solutions
Lecture Notes