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Abscract
1.
Machine Learning Basics
1.1.
Perceptron and PLA
1.2.
Why machine can learn
1.3.
Linear regression
1.4.
Logistic regression
1.5.
Feature Transform
1.6.
Overfitting
1.7.
Regularization
1.8.
Validation
2.
SVM
2.1.
Hard margin prime
2.2.
Hard margin dual
2.3.
Soft margin prime
2.4.
Soft margin dual
2.5.
Support vector recression
3.
Blending and Bagging
4.
Adaboost
5.
Decision Tree and Forest
6.
Gradient Boosted Decision Tree
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NTU ML14 notes