Machine Learning

Learn about machine learning, the area of artificial intelligence (AI) that is concerned with computational artifacts that modify and improve performance through experience.

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Created by: Charles Isbell

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Course Description

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.
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

Intermediate Level

Time: 14 Weeks

Instructor Details

Charles Isbell

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Dr. Isbell's research passion is artificial intelligence. In particular, he focuses on applying statistical machine learning to building autonomous agents that must live and interact with large numbers of other intelligent agents, some of whom may be human. Lately, Dr. Isbell has turned his energies toward adaptive modeling, especially activity discovery (as distinct from activity recognition); scalable coordination; and development environments that support the rapid prototyping of adaptive agents. As a result, he has begun developing adaptive programming languages, worrying about issues of software engineering, and trying to understand what it means to bring machine learning tools to non-expert authors, designers, and developers.