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 
2/21  topic 1: how can machines learn by embedding numerous features? linear support vector machine  course slides; LFD e8.1  
2/28  no class because of Massacre Rememberance Day  
3/7  dual support vector machine  course slides; LFD e8.2  
3/14  kernel support vector machine  course slides; LFD e8.3  
3/21  softmargin support vector machine  homework 1 announced  course slides; LFD e8.4 
3/28  kernel logistic regression  course slides; extended reading:  
4/4  no class because of Spring Break  
4/11  support vector regression  homework 1 due; homework 2 announced; final project announced  course slides; extended reading: 
4/18  topic 2: how can machines learn by combining predictive features? blending and bagging 
course slides; extended reading:  
4/25  adaptive boosting  course slides; extended reading:  
5/2  decision tree  homework 2 due; homework 3 announced  course slides; extended reading:

5/9  random forest  course slides; extended reading:  
5/16  gradient boosted decision tree  course slides; extended reading:  
5/23  topic 7: how can machines learn by distilling hidden features? neural network 
homework 3 due; homework 4 announced  course slides; LFD e7.1, e7.2, e7.3, e7.4 (selected parts) 
5/30  no class because of Dragon Boat Festival  
6/6  deep learning  course slides; LFD e7.6  
6/13  radial basis function network  course slides; LFD e6.3  
6/20  matrix factorization and finale  homework 4 due  course slides; course slides 