Machine Learning: Unsupervised Learning

Machine Learning: Unsupervised Learning, Conversations on Analyzing Data.

This is the second course in the Machine Learning Series, which consists of three courses. It is offered at Georgia Tech under course number CS7641. Georgia Tech credit is not awarded for taking this course at this location.

Have you ever wondered how Netflix is able to anticipate which movies you would enjoy? Alternatively, how does Amazon know what you want to buy even before you do? Unsupervised Learning holds the key to finding the solution!

Unsupervised Learning, which is closely related to pattern recognition, is the process of examining data and looking for patterns. If you are looking for structure in data, this is a very effective tool. A primary focus of this course is how to apply Unsupervised Learning techniques to discover structure in unlabeled data, including randomized optimization, clustering, feature selection and transformation, and feature selection and transformation.

Information about the series: Machine Learning is a graduate-level series of three courses that cover the topic of Artificial Intelligence that is concerned with computer programs that adapt and enhance their performance as a result of their interactions with the world.

It is taught as an interesting debate between two distinguished Machine Learning professors and friends: Professor Charles Isbell (Georgia Tech) and Professor Michael Littman (University of California, Berkeley) (Brown University).