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!

Course Cost
Free
Timeline
Approx. 4 months
Skill Level
Intermediate
Included in Course
  • Icon course 01 3edf6b45629a2e8f1b490e1fb1516899e98b3b30db721466e83b1a1c16e237b1 Rich Learning Content

  • Icon course 04 2edd94a12ef9e5f0ebe04f6c9f6ae2c89e5efba5fd0b703c60f65837f8b54430 Interactive Quizzes

  • Icon course 02 2d90171a3a467a7d4613c7c615f15093d7402c66f2cf9a5ab4bcf11a4958aa33 Taught by Industry Pros

  • Icon course 05 237542f88ede3178ac4845d4bebf431ddd36d9c3c35aedfbd92e148c1c7361c6 Self-Paced Learning

  • Icon course 03 142f0532acf4fa030d680f5cb3babed8007e9ac853d0a3bf731fa30a7869db3a 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

by Georgia Institute of Technology

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

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Course Leads

  • Michael Littman
    Michael Littman

    Instructor

  • Charles Isbell
    Charles Isbell

    Instructor

  • Pushkar Kolhe
    Pushkar Kolhe

    Instructor

What You Will Learn

Supervised Learning

  • Lesson 0: Machine Learning is the ROX
  • Lesson 1: Decision Trees
  • Lesson 2: Regression and Classification
  • Lesson 3: Neural Networks
  • Lesson 4: Instance-Based Learning
  • Lesson 5: Ensemble B&B
  • Lesson 6: Kernel Methods and Support Vector Machines (SVM)s
  • Lesson 7: Computational Learning Theory
  • Lesson 8: VC Dimensions
  • Lesson 9: Bayesian Learning
  • Lesson 10: Bayesian Inference

Unsupervised Learning

  • Lesson 1: Randomized optimization
  • Lesson 2: Clustering
  • Lesson 3: Feature Selection
  • Lesson 4: Feature Transformation
  • Lesson 5: Information Theory

Reinforcement Learning

  • Lesson 1: Markov Decision Processes
  • Lesson 2: Reinforcement Learning
  • Lesson 3: Game Theory
  • Lesson 4: Game Theory, Continued

Prerequisites and Requirements

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).

See the Technology Requirements for using Udacity.

Why Take This Course

You will learn about and practice a variety of Supervised, Unsupervised and Reinforcement Learning approaches.

Supervised Learning is an important component of all kinds of technologies, from stopping credit card fraud, to finding faces in camera images, to recognizing spoken language. Our goal is to give you the skills that you need to understand these technologies and interpret their output, which is important for solving a range of data science problems. And for surviving a robot uprising.

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 section focuses on how you can use Unsupervised Learning approaches -- including randomized optimization, clustering, and feature selection and transformation -- to find structure in unlabeled data.

Reinforcement Learning is the area of Machine Learning concerned with the actions that software agents ought to take in a particular environment in order to maximize rewards. You can apply Reinforcement Learning to robot control, chess, backgammon, checkers, and other activities that a software agent can learn. Reinforcement Learning uses behaviorist psychology in order to achieve reward maximization. This section also includes important Reinforcement Learning approaches like Markov Decision Processes and Game Theory.

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

Thanks for your interest!

We'll be in touch soon.

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