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EECS 498: Principles of Machine Learning
Note: beginning 2023, this course will be EECS 453
Instructor: Prof. Laura Balzano, Prof. Qing Qu, Prof. Lei Ying
Coverage
The class will cover basic principles in machine learning, such as unsupervised learning (e.g., clustering, mixture models, dimension reduction), supervised learning (e.g., regression, classification, neural networks & deep learning), and reinforcement learning. For each topic, key algorithmic ideas/intuitions and basic theoretical insights will be highlighted.
This is an entry-level machine learning course targeted for senior undergraduate and junior master students. This course has a little bit more emphasis on mathematical principles in comparison to EECS 445. Students outside the ECE program interested in machine learning are welcome as well!
Prerequisite
EECS 351, or EECS 301, or any linear algebra courses