Stanford machine learning course handout

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INSTRUCTOR: Andrew Ng

Http://see.stanford.edu/see/courseinfo.aspx? Coll = 348ca38a-3a6d-4052-937d-cb017338d7b1

Http://www.stanford.edu/class/cs229/materials.html

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