Georgia Tech OMSCS: Machine Learning and Reinforcement Learning
A Review of Machine Learning and Reinforcement Learning.
Over the past two years, I’ve been taking courses at Georgia Tech while working full-time. During this time, I was traveling, consulting, and working at a startup. If you have a similar workload or situation that requires 40–50 hours’ worth of full-time work a week, this may help you. This blog reviews popular courses for anyone in the OMSCS program with the Machine Learning specialization.
Let’s start with Reinforcement Learning.
CS7642: Reinforcement Learning and Decision Making
Out of all the courses that I’ve taken during this program, Reinforcement Learning has been the most engaging. It’s a machine learning course at the graduate level, so the expectation is that you have some experience in machine learning. It’s not the end of the world if you don’t. CS7641 Machine Learning would be the most helpful for easing the learning curve. But you have to start somewhere, and it’s not by any means an easy course.
The course consists of three components:
Component
Homework (6) 30% — Submitted as a quiz via Canvas.
Projects (3): 45% — Replicating results from recent relevant research papers.
Exams (1) 25% — Free form answers.
Assignments
I describe this course as abstract because all the projects don’t have a public rubric. The majority of your grade comes from your written analysis. This course depends on your ability to explain the fundamentals of reinforcement learning. The first half of the course covers Markov Decision Problems, Value/policy Iteration, and Q-learning. Then the course material builds on these concepts to discuss partially observable MDPs and game theory.
Time & Effort
I found myself spending 35–45 hours a week on this course. I took this course during the summer, and it was a bit of a time crunch working on the projects and homework. Gathering data and writing took as much time as designing and replicating the research papers assigned for each project. While…
