Statistical Data Mining Tutorials. Advertisment: In 2006 I joined Google.
We are growing a Google Pittsburgh office on CMU's campus. We are hiring creative computer scientists who love programming, and Machine Learning is one the focus areas of the office. We're also currently accepting resumes for Fall 2008 intenships. Background and Lead-in to SVM (svm15) CS 229: Machine Learning (Course handouts) Lecture notes 1 (ps) (pdf) Supervised Learning, Discriminative Algorithms Lecture notes 2 (ps) (pdf) Generative Algorithms Lecture notes 3 (ps) (pdf) Support Vector Machines Lecture notes 4 (ps) (pdf) Learning Theory Lecture notes 5 (ps) (pdf) Regularization and Model Selection Lecture notes 6 (ps) (pdf) Online Learning and the Perceptron Algorithm.
(optional reading) Lecture notes 7a (ps) (pdf) Unsupervised Learning, k-means clustering. Lecture notes 7b (ps) (pdf) Mixture of Gaussians Lecture notes 8 (ps) (pdf) The EM Algorithm Lecture notes 9 (ps) (pdf) Factor Analysis Lecture notes 10 (ps) (pdf) Principal Components Analysis Lecture notes 11 (ps) (pdf) Independent Components Analysis Lecture notes 12 (ps) (pdf) Reinforcement Learning and Control Supplemental notes 1 (pdf) Binary classification with +/-1 labels. Supplemental notes 2 (pdf) Boosting algorithms and weak learning.