Professor: Vassilis Athitsos
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
Contents:
- data - The datasets used for training across all of the assignments
- hw1 - Naive Bayes Classifier (n.b. not 100% correct)
- hw2 - Iterative Reweighted Least Squares linear classifier
- hw3 - Neural networks and backpropagation
- hw4 - Decision Trees
- hw5 - K-means clustering
- hw6 - Reinforcement learning with both the Value Iteration and Q-Learning algorithms