GS/EECS 5327 (3.00)

Introduction to Machine Learning

Description

This course extends the undergraduate machine learning to cover more advanced topics in machine learning. At the discretion of instructors, each course offering may select some advanced topics from, but not limited to, the following list: statistical learning theory, modern deep learning techniques, kernel methods, statistical models, deep generative models, Bayesian learning, graphical methods, reinforcement learning, meta learning, causal inference. Furthermore, the course also teaches how to apply and/or adapt these advanced machine learning methods to more realistic settings in artificial intelligence such as computer vision, natural language processing, robotics, computer games and bioinformatics and so on.

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