Global Arc

How can we decode what people are thinking by looking at their brain scans? Over the past several years, researchers have started to address this question by applying sophisticated pattern-classification algorithms to patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. In lectures, students will learn about cutting-edge techniques for finding meaningful patterns in large, noisy datasets; in weekly computer labs, students will use these techniques to gain insight into fMRI datasets.

Power electronics circuits are critical building blocks in a wide range of applications, ranging from mW-scale portable devices, W-scale telecom servers, kW-scale motor drives, to MW-scale solar farms. This course is a design-oriented course and will present fundamental principles of power electronics. Topics include: 1) circuit elements;2) circuit topology; 3) system modeling and control; 4) design methods and practical techniques. Numerous design examples will be presented in the class, such as solar inverters, data center power supplies, radio-frequency power amplifiers, and wireless power transfer systems.

The lectures will cover: (1) Basic principles of digital signal processing. (2) Design of digital filters. (3) Fourier analysis and the fast Fourier transform. (4) Roundoff errors in digital signal processing. (5) Applications of digital signal processing.

This course will present data analytics perspectives of electric power systems. The course offers an introduction to the basic concepts of power system operation and planning, along with necessary theories and methods in optimization. Topics include modeling and optimization of power networks, power flow analysis, state estimation and observability, bad data detection, introduction to the electricity market, and selected topics in smart grids. Strong emphasis will be placed on developing practical techniques to solve convex and stochastic optimization problems.

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.

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.