About This Course
*This is the second course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS7641. Taking this class here does not earn Georgia Tech credit.* Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? 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 you can use Unsupervised Learning approaches -- including randomized optimization, clustering, and feature selection and transformation -- to find structure in unlabeled data. **Series Information**: Machine Learning is a graduate-level series of 3 courses, covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences. - [Machine Learning 1: Supervised Learning](https://www.udacity.com/course/ud675) - [Machine Learning 2: Unsupervised Learning](https://www.udacity.com/course/ud741) (this course) - [Machine Learning 3: Reinforcement Learning](https://www.udacity.com/course/ud820) If you are new to Machine Learning, we suggest you take these 3 courses in order. The entire series is taught as an engaging dialogue between two eminent Machine Learning professors and friends: Professor Charles Isbell (Georgia Tech) and Professor Michael Littman (Brown University).
Why Take This?
You will learn about and practice a variety of Unsupervised Learning approaches, including: randomized optimization, clustering, feature selection and transformation, and information theory. You will learn important Machine Learning methods, techniques and best practices, and will gain experience implementing them in this course through a hands-on final project in which you will be designing a movie recommendation system (just like Netflix!).
Prerequisites and Requirements
We recommend you take [Machine Learning 1: Supervised Learning](https://www.udacity.com/ud675) prior to taking this course. This class will assume that you have programming experience as you will be expected to work with python libraries such as numpy and scikit. A good grasp of probability and statistics is also required. Udacity's [Intro to Statistics](https://www.udacity.com/course/st101), especially [Lessons 8, 9 and 10](https://www.udacity.com/course/viewer#!/c-st101/l-48738235/m-48688822), may be a useful refresher. An introductory course like Udacity's [Introduction to Artificial Intelligence](https://www.udacity.com/course/cs271) also provides a helpful background for this course.
Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!