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 
2010.09.06  (last week of summer!)  homework 0 released  
2010.09.13  introduction/perceptrons  homework 1 released; policy explained here 
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2010.09.20  perceptrons/setup/learnability  advantages announced 
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2010.09.27  learnability/generalization  homework 1 due; homework 2 released; sidework 1 on the proof of Hoeffding's inequality released 
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2010.10.04  generalization/VC dimension 
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2010.10.11  VC inequality/PLR revisited  homework 2 due; homework 3 released 
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2010.10.18  practical VC/linear classification 
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2010.10.25  linear model  homework 3 due; homework 4 released 
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2010.11.01  overfitting/regularization/validation 
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2010.11.08  validation/support vector machines  homework 4 due; homework 5 released 
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2010.11.15  (no classNTU anniversary!)  
2010.11.22  support vector machines  sidework 2 on KKT conditions released 
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2010.11.29  support vector machines/practical tools  homework 5 due; final project announced; homework 6 released; 
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2010.12.06  multiclass/aggregation/bagging 
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2010.12.13  boosting/decision trees  homework 6 due; homework 7 released 
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2010.12.20  decision trees/random forest/feature selection 
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2010.12.27  Bayesian approaches  homework 7 due; optional homework 8 released  taught in class: 
2011.01.03  Bayesian approaches  homework 8 due 
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2011.01.10  summary/final project discussions  final project due 
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