CS/ECE/ME532, Matrix Methods in Machine Learning, fall 2019.
An introduction to machine learning that focuses on matrix methods and features real-world applications ranging from classification and clustering to denoising and data analysis. Mathematical topics covered include: linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Machine learning topics include: the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. In addition to the formal course requisites, students are expected to have had exposure to numerical computing (e.g. Matlab, Python, Julia, R). Appropriate for graduate students or advanced undergraduates.
CS/ECE/ME532, Matrix Methods in Machine Learning, summer 2019 (with Professor Van Veen).
ECE203, Signals, Information and computing, spring 2014 (taught as a post doctoral researcher).
3 credit course, enrollment 102 students. An introduction to the fascinating world of signals, information, and computing. Learn about the algorithms and mathematical foundations that underpin how signals are acquired, analyzed and processed in applications ranging from MP3 players to brain imaging using MRI. We will explore signal processing not only with mathematics, but also through hands-on experiments with real-world signals.