4309-Machine-Learning

This course offers an introduction to machine learning. Topics include naive Bayes classifiers, linear regression, linear classificiers, neural networks and backpropagation, kernel methods, decision trees, feature selection, clustering, and reinforcement learning. A strong programming background is assumed, as well as familiarity with linear algebra (vector and matrix operations), and knowledge of basic probability theory and statistics.

Assignments

1. Nth Smallest
2. Naive Bayes
3. Linear Regression
4. Neural Networks
5. Decision Trees
6. Clustering
7. Markov Decision Processes