Public profile
Research areas
Computational Imaging, Computer Vision, Machine Learning, Applied Optics, Artificial Intelligence, Imaging
Electrical and Computer Engineering
Assistant Research Professor
Member, Ken Kennedy Institute
Average rating
3.4
15 temporary mock ratings
Difficulty
3.3
course-linked average
Courses
3
in seeded sections
Computational Imaging, Computer Vision, Machine Learning, Applied Optics, Artificial Intelligence, Imaging
ELEC 478
This course is an advanced introduction to concepts, methods, best practices, and theoretical foundations of machine learning. Topics covered include regression, classification, regularization, kernels, clustering, dimension reduction, decision trees, ensemble learning, and neural networks. Cross-list: ELEC 578. Mutually Exclusive: Cannot register for ELEC 478 if student has credit for COMP 540/DSCI 303/ELEC 578/STAT 413/STAT 613.
ELEC 578
This course is a graduate level introduction to concepts, methods, best practices, and theoretical foundations of machine learning. Topics covered include regression, classification, regularization, kernels, clustering, dimension reduction, decision trees, ensemble learning, and neural networks. Additional work is required for graduate students beyond the undergraduate requirement. Cross-list: ELEC 478. Recommended Prerequisite(s): Basic statistics and probability, linear algebra, and programming in R or Python are required. Mutually Exclusive: Cannot register for ELEC 578 if student has credit for DSCI 303/ELEC 478.
ELEC 800
Repeatable for Credit.