Machine Learning Applications

Description

The course was intended to introduce students to the fundamental of machine learning. As a TA, I had to conduct exam and evaluate them.

Course Content

1. Bayesian decision theory
2. Linear models for regression and classification
3. Support Vector Machines and kernel methods
4. Decision tree based methods
5. Expectation Maximization and associated algorithms
6. Unsupervised learning methods including various clustering algorithms
7. Maximum likelihood, Bayesian estimation
8. Selected advanced topics

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