ECE 344/444: Introduction to Online and Reinforcement Learning
In today’s cyber-physical-human world, learning to make optimal decisions in interactive systems is critical. Reinforcement learning is an important and powerful paradigm for doing so, with wide ranging applications including robotics, energy distribution, game playing, consumer modeling and healthcare.
This class covers the basics of reinforcement learning starting with the framework (actions in response to changing environment), and bandit problems, we study different methods within that framework including dynamic programming, Monte Carlo methods, temporal difference and Q-learning. While this course is targeted at students with some mathematical background (junior engineering level), we cover the basics of probability and random processes that are essential to understanding the reinforcement learning framework. Through the course we explore both the theory and practice of reinforcement learning using relevant example applications.
The course is lecture based, with both written and coding (Python) assignments, and a project.
ENGR 005: Intro to Engineering Practice (Electrical and Computer Engineering)
Electrical and Computer engineering is a hugely diverse field that is at the forefront of almost every major innovation in recent times and has a significant impact on almost every grand challenge in the twenty first century. In this introductory freshman experience, we introduce the field through a series of hands-on laboratory experiments that start with basic circuit ideas, and enable the students to build prototypes for a range of applications including remote traffic light systems, audio processors, wireless networks, bio-sensing, and cryptographic channels. Aside from passive and active circuit elements, we use Raspberry Pi processors and some simple Python coding to construct the elements of modern day cyber-physical engineering systems.