Wednesday, July 7, 2010

Machine Learning - Tutorial & Stanford Lecture Videos

What is Machine Learning?
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. - Tom M. Mitchell (1997).

Following are the recommended prerequisites for this course:

Lecture handouts
cs229-notes1.pdf Linear Regression, Classification and logistic regression, Generalized Linear Models
cs229-notes2.pdf Generative Learning algorithms
cs229-notes3.pdf Support Vector Machines
cs229-notes4.pdf Learning Theory
cs229-notes5.pdf Regularization and model selection
cs229-notes6.pdf The perceptron and large margin classifiers
cs229-notes7a.pdf The k-means clustering algorithm
cs229-notes7b.pdf Mixtures of Gaussians and the EM algorithm
cs229-notes8.pdf The EM algorithm
cs229-notes9.pdf Factor analysis
cs229-notes10.pdf Principal components analysis
cs229-notes11.pdf Independent Components Analysis
cs229-notes12.pdf Reinforcement Learning and Control

Machine Learning - Standford University Lectures

A Big thanks to the Standford University for posting these lectures.


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