Although students may understand AI techniques in principle by reading the book and attending lectures, there is no substitute for the sort of understanding one obtains by actually implementing a technique and trying it out on a problem, even if the problem is small relative to the sort of problems we wish AI system to tackle.  To this end, the course will entail 8 homework assignments. For the assignments inovlving programming, you may use whatever language you are most comfortable with -- Java, C++, python, matlab, etc.

Because the course schedule is tentative, homework due dates may change.  However, when homework is assigned, the due date will be indicated, and it is not flexible.  Late assignments will not be accepted.  Assignments must be turned in at the start of class on the assignment due date.  If you anticipate any difficulty turning in your homework on time, let your professor know at the time when it is assigned.

Tentatively, the homeworks will be weighted as follows:

Homework #	Topic	Weighting
1	Research existing AI systems	5%
2	Wumpus world agent	10%
3	Probability theory	10%
4	Probabilistic inference	10%
5	Reinforcement learning	15%
6	Simple classifiers	10%
7	Neural networks	15%
8	Final project	25%

We will have a final exam, at the time scheduled by the university, which is Saturday December 11, 7:30 p.m. - 10 p.m.  The exam will be cumulative, and will focus on evaluating your understanding of material that was presented in the course but not central to the homework assignments (e.g., guest lectures). 

Semester grades will be based 80% on the homework assignments, 20% on the final.