Theory and Applications of Pattern Recognition

ECE/CS/ME 532, Fall 2016–17
University of Wisconsin–Madison

This course is 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. Students are expected to have taken a course in vector calculus, such as MATH 234, and have exposure to numerical computing (Matlab, Python, Julia, or equivalent). Appropriate for graduate students or advanced undergraduates.

Lectures: Tue/Thu, 2:30pm–3:45pm, Engineering Centers Building (ECB), Room 1003.
Instructor: Laurent Lessard. Office hours: Tue 3:45–5:00pm, ECB 1003 (after lecture)
Teaching Assistant: Xiaomin Zhang. Office hours: Fri 4:00–6:30 pm, CS 3310.

Submitting assignments: Gradescope.
Discussion forum: Piazza.

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