Classification Lecture: slides

Useful Links:

• Machine learning class - CMU: Look at their slides about logistic regression, Naive Bayes, SVM and decision trees.
Machine learning article - Wikipedia: Contains pointers to individual articles (like SVM), as well as useful software resources.

SVMs

• Good tutorial paper: Support Vector Machines: Hype or Hallelujah? - Kristin Bennet, Colin Campbell, SIGKDD Explorations, 2,2, 2000, 1-13
• Andrew Moore's slides on SVM
• A practical guide to SVM classification - From the authors of libSVM

Decision Trees

• Tom Mitchell's slides on decision trees
• Breiman's website on Random Forests

Reading

• Coverage of all of the methods that we discussed in the lecture and more:
  Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Second Edition), NY: Springer.


• We didn't discuss theory, but if you're interested in the theory of (binary) classification, here's a pointer to get you started:
  Bartlett, P., Jordan, M. I., & McAuliffe, J. D. (2006). Convexity, classification and risk bounds. Journal of the American Statistical Association, 101, 138-156.