Study sheet for final exam

The final exam will be given on Tuesday, December 19, from 10:00 to 11:50 in room 513. The exam will be open book and open notes. It will be a cumulative exam, covering the entire semester, but it will emphasize the topics that were not covered on the mid-term. I will not ask about any material that I did not cover in class.

If you will be unable to take the exam at the scheduled time, or if you have two or more other exams scheduled for the same day, please let me know as soon as possible. If you feel you need to take an incomplete in the class, please consult with me as soon as possible, and no later than the last class meeting on Tue. December 12. If you do not let me know in advance, and you do not show up to the exam, I will allow you to take a make-up only if you missed the exam due to a medical problem or dire family emergency. In such a case you will be expected to take the make-up as soon as possible. Under no circumstances will I grant an extended incomplete in the course if you do not arrange it with me prior to December 12.

The solutions to the exam will be posted on the class web site immediately after the exam. The exams will be graded and the course grades computed within a few days of the exam. Send me email if you want to know your grade.

Topics on the final exam include:

You should know the following algorithms well and tbe able to carry them out on the exam: Depth-first search, breadth-first search, iterative deepening, hill-climbing, alpha-beta pruning, conversion of propositional sentences to CNF, Davis-Putnam, forward and backward chaining in Datalog, chart parsing, 1R, nearest neighbors, Naive Bayes, classification using perceptrons and feed-forward.

You should know the ID3 algorithms in detail and be able to carry out any aspect of the algorithm that does not depend on actually calculating entropies.

You should know the general structure and issues involved in the following algorithms, but you need not memorize the fine details: Perceptron learning, and back-propagation learning.

The following topics were discussed or will be discussed in class, but will not be on the final exam: Entropy, information theory, clustering, any material on planning that I discuss in the last two lectures.