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Good Freely Available Textbooks on Machine Learning - MetaOptimize Q+A
Introduction to Machine Learning: Draft of Incomplete Notes by Nils J. Nilsson Although I have tried to eliminate errors, some undoubtedly remain---caveat lector. Certain elements of the typography (overflow into margins, etc.) have not been polished. The notes survey many of the important topics in machine learning circa the late 1990s.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.
