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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.
