• Length:
    14 Weeks
  • Effort:
    8–10 hours per week
  • Price:

    FREE
    Add a Verified Certificate for $99 USD

  • Institution
  • Subject:
  • Level:
    Intermediate
  • Language:
    English
  • Video Transcript:
    English

About this course

Skip About this course

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. This area is also concerned with issues both theoretical and practical.

In this course, we will present algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including:

  • statistical supervised and unsupervised learning methods
  • randomized search algorithms
  • Bayesian learning methods
  • reinforcement learning

The course also covers theoretical concepts such as inductive bias, the PAC and Mistake‐bound learning frameworks, minimum description length principle, and Ockham's Razor. In order to ground these methods the course includes some programming and involvement in a number of projects.

By the end of this course, you should have a strong understanding of machine learning so that you can pursue any further and more advanced learning.

This is a three-credit course.

What you'll learn

Skip What you'll learn

There are four primary objectives for the course:

  • To provide a broad survey of approaches and techniques in machine learning;
  • To develop a deeper understanding of several major topics in machine learning;
  • To develop the design and programming skills that will help you to build intelligent, adaptive artifacts;
  • To develop the basic skills necessary to pursue research in machine learning.
Week 1: ML is the ROX/SL 1- Decision Trees
Week 2: SL 2- Regression and Classification
Week 3: SL 3- Neutral Networks
Week 4: SL 4- Instance Based Learning
Week 5: SL 5- Ensemble B&B
Week 6: SL 6- Kernel Methods & SVMs
Week 7: SL 7- Comp Learning Theory
Week 8: SL 8- VC Dimensions
Week 9: SL9- Bayesian Learning
Week 10: SL 10- Bayesian Inference
Week 11: UL 1- Randomized Optimization
Week 12: UL 2- Clustering/ UL 3- Feature Selection
Week 13: UL 4- Feature Transformation/UL 5- Info Theory
Week 14: RL 1- Markov Decision Processes
Week 15: Reinforcement Learning
Week 16: RL 3 Game Theory/Outro

Meet your instructors

Charles Isbell
Executive Associate Dean and Professor
The Georgia Institute of Technology

Pursue a Verified Certificate to highlight the knowledge and skills you gain $99.00

View a PDF of a sample edX certificate
  • Official and Verified

    Receive an instructor-signed certificate with the institution's logo to verify your achievement and increase your job prospects

  • Easily Shareable

    Add the certificate to your CV or resume, or post it directly on LinkedIn

  • Proven Motivator

    Give yourself an additional incentive to complete the course

  • Support our Mission

    EdX, a non-profit, relies on verified certificates to help fund free education for everyone globally

Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.