1. To support our site, MoocLab may be compensated by some course providers through affiliate links.

edX Machine Learning

Georgia Institute of Technology via edX

  • Overview
  1. edX
    Platform:
    edX
    Provider:
    Georgia Institute of Technology
    Length:
    14 weeks
    Effort:
    8 to 10 hours per week
    Language:
    English
    Credentials:
    Paid Certificate Available
    Overview
    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 Mistakeā€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
    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.
    Syllabus
    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

    Taught by
    Charles Isbell
    Go to Course:

Share This Page



  1. This site uses cookies to help personalise content, tailor your experience and to keep you logged in if you register.
    By continuing to use this site, you are consenting to our use of cookies.
    Dismiss Notice