Table of Contents
1. Introduction to Machine Learning
5. Perceptron
7. Naive Bayes
11. Hierarchical Agglomerative Clustering
12. Principal Component Analysis
13. Linear Discriminant Analysis
14. Subset Selection
17. Regularization for Neural Network