CS 7641: Machine Learning

Instructional Team

Charles Isbell

Charles Isbell
Creator, Instructor
Michael Littman

Michael Littman
Dan Boros

Dan Boros
Head TA
Sunmin Lee

Sunmin Lee
Head TA
John Mansfield

John Mansfield
Head TA


This is a 3-course Machine Learning Series, taught as a dialogue between Professors Charles Isbell (Georgia Tech) and Michael Littman (Brown University).

Supervised Learning
Supervised Learning is a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a number of fascinating things. This sort of machine learning task is an important component in all kinds of technologies. From stopping credit card fraud; to finding faces in camera images; to recognizing spoken language - our goal is to give students the skills they need to apply supervised learning to these technologies and interpret their output. This is especially important for solving a range of data science problems.

Unsupervised Learning
Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy, before you make a purchase? The answer can be found in Unsupervised Learning. Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This course focuses on how students can use Unsupervised Learning approaches - including randomized optimization, clustering, and feature selection and transformation - to find structure in unlabeled data.

Reinforcement Learning
Reinforcement Learning is the area of Machine Learning concerned with the actions that software agents ought to take in a particular environment in order to maximize rewards. You can apply Reinforcement Learning to robot control, chess, backgammon, checkers and other activities that a software agent can learn. Reinforcement Learning uses behaviorist psychology in order to achieve reward maximization.

This course counts towards the following specialization(s):
Computational Perception and Robotics
Interactive Intelligence
Machine Learning

Foundational Course
Interactive Intelligence Specialization Core
Machine Learning Specialization Elective
Computational Perception & Robotics Specialization Elective


Course Overview

Sample Lesson

Sample Syllabus

Fall 2022 syllabus and schedule (PDF)
Spring 2022 syllabus (PDF)

Note: Sample syllabi are provided for informational purposes only. For the most up-to-date information, consult the official course documentation.

Course Videos

You can view the lecture videos for this course here.

Before Taking This Class...

Suggested Background Knowledge

An introductory course in artificial intelligence is recommended but not required. To discover whether you are ready to take CS 7641: Machine Learning, please review our Course Preparedness Questions, to determine whether another introductory course may be necessary prior to registration.

Technical Requirements and Software
  • Browser and connection speed: An up-to-date version of Chrome or Firefox is strongly recommended. We also support Internet Explorer 9 and the desktop versions of Internet Explorer 10 and above (not the metro versions). 2+ Mbps is recommended; the minimum requirement is 0.768 Mbps download speed.
  • Operating system:
    • PC: Windows XP or higher with latest updates installed
    • Mac: OS X 10.6 or higher with latest updates installed
    • Linux: any recent distribution that has the supported browsers installed

Academic Integrity

All Georgia Tech students are expected to uphold the Georgia Tech Academic Honor Code. This course may impose additional academic integrity stipulations; consult the official course documentation for more information.