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.

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).

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Course Cost
Free
Timeline
Approx. 1 month
Skill Level
Intermediate
Included in Course
  • Rich Learning Content

  • Interactive Quizzes

  • Taught by Industry Pros

  • Self-Paced Learning

  • Student Support Community

Join the Path to Greatness

This free course is your first step towards a new career with the Machine Learning Engineer Nanodegree Program.

Free Course

Machine Learning: Unsupervised Learning

by Georgia Institute of Technology

Enhance your skill set and boost your hirability through innovative, independent learning.

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Course Leads

  • Charles Isbell
    Charles Isbell

    Instructor

  • Michael Littman
    Michael Littman

    Instructor

  • Pushkar Kolhe
    Pushkar Kolhe

    Instructor

What You Will Learn

Lesson 1: Randomized optimization

  • Optimization, randomized
  • Hill climbing
  • Random restart hill climbing
  • Simulated annealing
  • Annealing algorithm
  • Properties of simulated annealing
  • Genetic algorithms
  • GA skeleton
  • Crossover example
  • What have we learned
  • MIMIC
  • MIMIC: A probability model
  • MIMIC: Pseudo code
  • MIMIC: Estimating distributions
  • Finding dependency trees
  • Probability distribution

Lesson 2: Clustering

  • Clustering and expectation maximization
  • Basic clustering problem
  • Single linkage clustering (SLC)
  • Running time of SLC
  • Issues with SLC
  • K-means clustering
  • K-means in Euclidean space
  • K-means as optimization
  • Soft clustering
  • Maximum likelihood Gaussian
  • Expectation Maximization (EM)
  • Impossibility theorem

Lesson 3: Feature Selection

  • Algorithms
  • Filtering and Wrapping
  • Speed
  • Searching
  • Relevance
  • Relevance vs. Usefulness

Lesson 4: Feature Transformation

  • Feature Transformation
  • Words like Tesla
  • Principal Components Analysis
  • Independent Components Analysis
  • Cocktail Party Problem
  • Matrix
  • Alternatives

Lesson 5: Information Theory

  • History -Sending a Message
  • Expected size of the message
  • Information between two variables
  • Mutual information
  • Two Independent Coins
  • Two Dependent Coins
  • Kullback Leibler Divergence

Unsupervised Learning Project

Prerequisites and Requirements

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.

See the Technology Requirements for using Udacity.

Why Take This Course

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!).

What do I get?
  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals
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