Matrix Methods in Machine Learning (ECE 532), a highly popular course, will be offered online for the first time during Fall semester 2019! This core course for Master of Engineering Data Analytics (MEDA) students is also available as an elective for students in some of our other online graduate engineering programs. Make sure to check your particular degree program requirements.
Machine learning is about training computers to make decisions or predictions based on example data. Successful machine learning requires not only strong programming skills, but also a fundamental understanding of the underlying mathematical models, their appropriateness for different applications, and their strengths and limitations.
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.
If you’re not sure whether you can take this course as part of your program, then make sure to contact your EPD program director or student services team. Enrollment in the course is limited. If interested please enroll in the ECE 532, section 003 taught by Barry Van Veen, as soon as possible to reserve your place in the course! We are trying to estimate likely enrollment and ensure adequate support for enrolled students. Please direct any questions to studentservices@epd.wisc.edu.
On Wisconsin!