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).

Prerequisites
Following are the recommended prerequisites for this course:

• Normal distribution
• Gaussian function
• Basic probability
  
• Derivatives
  

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

• Lecture 1
  


• Lecture 2
  


• Lecture 3
  


• Lecture 4
  


• Lecture 5
  


• Lecture 6
  


• Lecture 7
  


• Lecture 8
  


• Lecture 9
  


• Lecture 10
  


• Lecture 11
  


• Lecture 12
  


• Lecture 13
  


• Lecture 14
  


• Lecture 15
  


• Lecture 16
  


• Lecture 17
  


• Lecture 18
  


• Lecture 19
  


• Lecture 20
  

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