Week 1

Introduction to ML

Reinforcement Learning

Unsupervised Learning

Supervised Learning

Week 2

Linear Regression

Multivariate Regression

Partial Least Squares

Shrinkage Methods

Week 3

Linear Discriminant Analysis

Linear Classification

Logistic Regression

Project

Week 4

Support Vector Machines

Hinge Loss Formulation

Perceptron Learning

Week 5

Artificial Neural Networks

Training and Validation

Parameter Estimations

Week 6

Regression Trees

Decision Trees

Decision Trees Examples

Week 7

ROC Curve

Evaluation Measures

Ensemble Methods

Minimum Desc. Lgth Analysis

Week 8

Random Forests

Bayesian Networks

Gradient Boosting

Naive Bayes

Week 9

Hidden Markov Models

Treewidth and belief

Undirected Graphical Method

Variable Elimination

Week 10

Clustering

Birch and Cure Algorithms

Week 11

Expectation Maximization

Gaussian Mixture Models

Week 12

Reinforcement Learning

Linear Theory