Machine Learning

Learn Supervised, Unsupervised and Reinforcement Learning approaches from entertaining and competent instructors. Offered at Georgia Tech, this free and interactive course covers an interesting area of Artificial Intelligence.

Created by: Michael Littman

Produced in 2015

What you will learn

  • Supervised Learning to understand how your phone can recognize your voice
  • Algorithms that Netflix uses to accurately recommend movies
  • Apply Reinforcement Learning to control robots or win a game of chess
  • Kernel methods, including Support Vector Machines (SVM)
  • Where and how to use Bayesian inference techniques
  • Clustering, Feature transformation and selection
  • Demystify Markov decision processes
  • How do Machine Learning and Game Theory intersect
  • Much, Much more!

Quality Score

Content Quality
Video Quality
Qualified Instructor
Course Pace
Course Depth & Coverage

Overall Score : 94 / 100

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Course Description

This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree.Machine Learning is a graduate-level course covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.The first part of the course covers Supervised Learning, 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 bunch of other cool stuff.In part two, you will learn about Unsupervised Learning. Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? Such answers can be found in this section!Finally, can we program machines to learn like humans? This Reinforcement Learning section will teach you the algorithms for designing self-learning agents like us!



    • The course is a part of the Online Masters Degree at one of the best universities for computer science.
    • Charming and entertaining instructors.
    • Broad survey of the Machine Learning field.
    • Unique style of teaching that will not suit everyone.
    • Long and Time-consuming.

Instructor Details

Michael Littman

Michael L. Littman joined Brown University's Computer Science Department after ten years (including 3 as chair) at Rutgers University. His research in machine learning examines algorithms for decision making under uncertainty. Littman has earned multiple awards for teaching and his research has been recognized with three best-paper awards on the topics of meta-learning for computer crossword solving, complexity analysis of planning under uncertainty, and algorithms for efficient reinforcement learning. He has served on the editorial boards of the Journal of Machine Learning Research and the Journal of Artificial Intelligence Research. In 2013, he was general chair of the International Conference on Machine Learning (ICML) and program co-chair of the Association for the Advancement of Artificial Intelligence Conference and he served as program co-chair of ICML 2009.



3 total reviews

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By Hosea Siu on 8/31/2018

I watched most of the videos and did some of the work for work for Georgia Techs Udacity course on ML when I was first starting to learn about ML. I thought it was pretty good.

By Niranjan Upreti on 10/8/2015

It is an exhaustive course. You can learn a lot if you can handle the projects and assignments. You will have to devote a significant time but you will learn a lot.

By Ilya O on 10/12/2017

The lectors are great, and I particularly liked the cross-references and similarities between different topics that they show.