A strong familiarity with Probability Theory, Linear Algebra and Statistics is required. An understanding of Intro to Statistics, especially Lessons 8, 9 and 10, would be helpful. Students should also have some experience in programming (perhaps through Introduction to CS) and a familiarity with Neural Networks (as covered in Introduction to Artificial Intelligence).
About this Course: 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!