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
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