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.
|2/21||topic 1: how can machines learn by embedding numerous features?|
linear support vector machine
|course slides; LFD e-8.1|
|2/28||no class because of Massacre Rememberance Day|
|3/7||dual support vector machine||course slides; LFD e-8.2|
|3/14||kernel support vector machine||course slides; LFD e-8.3|
|3/21||soft-margin support vector machine||homework 1 announced||course slides; LFD e-8.4|
|3/28||kernel logistic regression||course slides; |
|4/4||no class because of Spring Break|
|4/11||support vector regression||homework 1 due; homework 2 announced; final project announced||course slides; |
|4/18||topic 2: how can machines learn by combining predictive features?
blending and bagging
|course slides; |
|4/25||adaptive boosting||course slides; |
|5/2||decision tree||homework 2 due; homework 3 announced||course slides; |
|5/9||random forest||course slides; |
|5/16||gradient boosted decision tree||course slides; |
|5/23||topic 7: how can machines learn by distilling hidden features?
|homework 3 due; homework 4 announced||course slides; LFD e-7.1, e-7.2, e-7.3, e-7.4 (selected parts)|
|5/30||no class because of Dragon Boat Festival|
|6/6||deep learning||course slides; LFD e-7.6|
|6/13||radial basis function network||course slides; LFD e-6.3|
|6/20||matrix factorization and finale||homework 4 due||course slides; course slides|