Über diesen Kurs

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.

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).


Rich Learning Content

Interactive Quizzes

Taught by Industry Pros

Self-Paced Learning

Student Support Community

Begib' dich auf den Weg des Erfolgs

Dieser kostenlose Kurs ist der erste Schritt auf dem Weg zu einer neuen Karriere mit dem Machine Learning Engineer Programm.

Kostenlose Kurse

Maschinelles Lernen: Unsupervised Learning

mit Georgia Institute of Technology

Erweitere deine Fähigkeiten und Karriere durch innovatives und unabhängiges Lernen.

Icon steps 54aa753742d05d598baf005f2bb1b5bb6339a7d544b84089a1eee6acd5a8543d


Charles Isbell
Charles Isbell


Michael Littman
Michael Littman


Pushkar Kolhe
Pushkar Kolhe



We recommend you take Machine Learning 1: Supervised Learning 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, especially Lessons 8, 9 and 10, may be a useful refresher.

An introductory course like Udacity's Introduction to Artificial Intelligence also provides a helpful background for this course.

Detaillierte technische Voraussetzungen

Was spricht für diesen Kurs?

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!).

Was bekomme ich?
Instructor videos Learn by doing exercises Taught by industry professionals