ELEC378 - Machine Learning: Concepts and Techniques
Instructor:  Prof. Richard Baraniuk

Machine learning is a powerful new way to build signal processing models and systems using data rather than physics. This introductory course covers the key ideas, algorithms, and implementations of both classical and modern methods. Topics include supervised and unsupervised learning, optimization, linear regression, logistic regression, support vector machines, deep neural networks, clustering, data mining. A course highlight is a hands-on team project competition using real-world data.

The course is open to students at all levels who are comfortable with linear algebra + coding in Python (ideally), R, MATLAB.

Course webpage; more information coming soon!