CS5804 Spring 2015 Final Topic List

This is a list of concepts that I’ll expect you to have a good understanding of now that you’re finishing the course. You don’t need to know the exact details of every concept, but having a rough idea of what each means and connections to other concepts will help you use AI ideas in your future work. For the final exam, since you’re free to use the book, your class notes, and my class materials, as well as any properly cited external materials from the Internet or other books, you certainly don’t need to memorize specific details.

Adversarial search


Probability and Bayesian networks

Markov decision processes

Reinforcement Learning

Machine learning

(Especially because I presented the material without a good accompanying text, I won’t be testing you on the details of the learning algorithms. Instead, I’ll test you on the macro-level concepts of machine learning: how supervised learning is set up, as described in 18.2 of R+N, and how to tune parameters via different forms of validation, as briefly discussed on the 1.5-page introduction of Section 18.4 in R+N.)

Topics covered in class not required for exam

These topics, especially the applications we discussed in class, may come up as themes in the "plots" of the exam problems, but you won’t be required to study them.