About this courseSkip 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.
At a glance
What you'll learnSkip 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 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