Schedule

You are responsible for coming prepared to class. This includes reading through the material before attending class. You will get a lot more out of the lectures and discussions in this manner. It is cliché, but true. Each week will follow a similar pattern. The course focuses both on the theory of data mining, machine learning, and its practical application. Most classes will start with an exam over the previous week’s material. followed by a lecture on new material. Followed by an interactive exercise focusing on the theory. Followed by an interactive lecture/exercise focusing on the application. This will sometimes be guided. sometimes in groups, and sometimes individually. The schedule below is tentative and subject to change. You must check it regularly.

The following are a list of topics and the order in which we will approach them. The finalized schedule will be posted each week to OAKS.

Week 1:
Syllabus discussion. Uninformed search.

Week 2 and 3:
A* Search and Heuristics

Week 4 and 5:
Constraint Satisfaction Problems

Week 6 and 7:
Game Trees: Minimax

Week 8 and 9:
Game Trees: Expectimax; Utilities

Week 10 and 11:
Markov Decision Processes and Hidden Markov Models

Week 12 and 13
Reinforcement Learning

Week 14 and 15
Natural Language Processing

Week 16:
Last week of classes!