Global Arc

An introduction to lossless data compression algorithms, modulation/demodulation of digital data, error correcting codes, channel capacity, lossy compression of analog and digital sources. Three hours of lectures. Prerequisites: 301, ORF 309.

Introduction to the basic theory and techniques of two- and three-dimensional image processing. Topics include image perception, 2-D image transforms, enhancement, restoration, compression, tomography and image understanding. Applications to HDTV, machine vision, and medical imaging, etc. Three hours of lectures, one laboratory.

Senior Thesis Course. The student has the opportunity to do a self driven project by proposing a topic and finding a faculty member willing to supervise the work, or, the student may do a project in conjunction with a faculty member's research. A second reader will be required for both the midterm report and final thesis report. Students will be required to enroll in ELE 498 in the spring.

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Provides an opportunity for a student to concentrate on a "state-of-the-art" project in electrical and computer engineering. A student may propose a topic and find a faculty member willing to supervise the work. Or the student may select a topic from lists of projects obtained from faculty and off-campus industrial researchers, subject to the consent of the faculty advisor.

Provides an opportunity for students to design their own projects and create them from start to finish. Topics will be proposed by students and must demonstrate proficiency in Electrical Engineering. This course is available by application only.

Reinforcement learning (RL) is a core technology at the heart of modern AI that learn to make good decisions in complex environments. It encompasses technologies such as continuous variable optimization, Q learning, neural networks, policy search, and bandit exploration. In this course, we aim to give an introductory overview of reinforcement learning, its core challenges, and approaches, including exploration and generalization. In parallel, we will present a collection of case studies from intelligent systems, games and healthcare. Students will learn through a combination of lectures, written assignments and coding assignments.