Practical machine learning (open classes)

1. Intro

1. Introduction to course.
2. Face classification problem (P1).
3. Nearest neighbor as example of supervised learning.
4. PCA, SVD and space dictionary learning as unsupervised learning.

2. Linear models I

1. What's inside numpy?
2. Linear regression
3. Binary linear classification
4. Stohastic gradient descent
5. House pricing problem (P2)

3. Linear models II

1. Multiclass linear classification
2. Linear models demo
3. SVM (support vector machine)

4. Bayesian classifier and density estimation

1. Multiclass Bayesian classification
2. Kernel density estimation
3. Parametric density estimation
4. Gaussian mixture model
5. Location problem (P3)

5. Decision trees

1. Classification and regression trees
2. Entropy criterion and Gini criterion
3. Random forest