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 
2009.09.14  introduction 

taught in class:

2009.09.21  introduction/perceptrons/learnability 

taught in class:

2009.09.28  generalization 

taught in class:

2009.10.05  generalization/VC inequality  homework 2 released 
taught in class:

2009.10.12  VC inequality/PLR resolved  homework 2 due 
taught in class:

2009.10.19  linear model  homework 3 released 
taught in class:

2009.10.26  linear model  homework 3 due 
taught in class:

2009.11.02  linear model/overfitting  homework 4 released 
taught in class:

2009.11.09  overfitting/Neural Network  good luck with your other midterms 
taught in class:

2009.11.16  Neural Network  homework 4 due 
taught in class:

2009.11.23  Neural Network/Support Vector Machine  homework 5 released 
taught in class:

2009.11.30  Bayesian and Unsupervised by Prof. Shoude Lin  taught in class:  
2009.12.07  Unsupervised and Machine Discovery by Prof. Shoude Lin  homework 5 due  taught in class: 
2009.12.14  Bayesian/Support Vector Machine  final project announced  
2009.12.21  Support Vector Machine/Bagging/Boosting  homework 6 released  
2009.12.28  Boosting  homework 6 due; homework 7 released  taught in class: 
2010.01.04  Boosting/Summary  homework 7 due  
2010.01.11  Summary/Final Project Discussions  final project due good luck with your other finals 
taught in class: 