Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the data observed. This course introduces the basics of learning theories, the design and analysis of learning algorithms, and some applications of machine learning.
date  syllabus  todo/done  suggested reading 
2008.09.18  introduction/unlearnability  homework 0 released; policy explained and released 

2008.09.25  learnability/perceptrons 
homework 1 released; homework guidelines announced 

2008.10.02  perceptron/linear regression  homework 1 due 

2008.10.09  optimization/generalization  homework 2 released 

2008.10.16  generalization/VC inequality  homework 2 due 

2008.10.23  generalization/nearest neighbors  homework 3 released 

2008.10.30  nearest neighbors/RBF Network/generalized linear model  homework 3 due 

2008.11.06  Neural Network  homework 4 released 

2008.11.13  Neural Network/Bayesian  (midterm week) 

2008.11.20  Bayesian  homework 4 due 

2008.11.27  Bayesian/Ensemble  homework 5 released 

2008.12.04  Bagging/Boosting  homework 5 due 

2008.12.11  Boosting  homework 6 released 

2008.12.18  SVM  homework 6 due; final project announced 

2008.12.25  SVM  homework 7 released  
2009.01.01  no class  New Year's Day (holiday)  
2009.01.08  summary; Q and A  homework 7 due  
2009.01.15  no class  (final week); final project due 
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