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.
Instructor  Head TA  TA  TA  TA  TA  TA  TA  
Name  HsuanTien Lin  KuChun Chou  Polone Chen  YuCheng Chou  WeiYuan Shen  ChunYen Ho  ChungLiang Li  YiHung Huang 
htlin AT csie . ntu . edu . tw 
ml2011ta AT csie . ntu . edu . tw 

Office Hour  after class in R102 or by appointment  Tuesdays 6:30pm to 7:30pm in R105; Fridays 10:00am to 11:00am in R217  
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date  syllabus  todo/done  suggested reading 
2010.09.12  MidAutumn Festival (no class)  homework 0 released; policy released  
2010.09.19  introduction  homework 1 released; policy explained 

2010.09.26  introduction  reasons to stay in class 

2010.10.03  introduction/generalization  homework 1 due; homework 2 released; sidework 1 released 

2010.10.10  DoubleTen Holiday (no class)  
2010.10.17  generalization  homework 2 due 

2010.10.24  generalization  homework 3 released 

2010.10.31  generalization/linear model 


2010.11.07  linear model  homework 4 released; good luck with your other midterms 

2010.11.14  linear model/overfitting  homework 3 due 

2010.11.21  regularization  homework 4 due; homework 5 released 

2010.11.28  validation/principles 


2010.12.05  support vector machine  homework 5 due; homework 6 released 

2010.12.12  support vector machine 


2010.12.19  support vector machine 


2010.12.26  aggregation  homework 6 due (12/30); homework 7 released 

2011.01.02  aggregation 


2011.01.09  summary  competition ends (1/8); homework 7 due (1/13); final project due (1/13); good luck with your other finals 
