CS 540: Introduction to Artificial Intelligence
Fall 2009 Exams 

Examinations: 
Midterm Exam: Wednesday, October 21, 7:15 p.m. - 9:15 p.m., room B130 in Van Vleck Hall.
Closed book.  Bring a calculator.  One 8.5x11 sheet of paper with notes on both 
sides allowed.  Covers topics in first half of the course, including readings, 
lectures and assignments. That is, covers Inductive Learning and Decision Trees 
(Chapter 18.1 - 18.3), Search (Chapters 3.1 - 3.5, 4.1 - 4.4 (except "Memory-
bounded heuristic search," pages 101-104), 5.1, 5.2, 6.1 - 6.3, and 6.5), and Logic 
(Chapters 7.1, 7.3 - 7.5, 8, and 9.1, 9.2, and 9.5). The following readings were 
assigned but will not be covered in the exam: Chapters 1, 2, 5.3, 7.2, 9.3 and 9.4. 
You are responsible for topics covered in the readings, except those explicitly 
excluded, even if they were not covered in class or in the lecture notes.

Final Exam: Tuesday, December 22, 5:05 p.m. - 7:05 p.m., Computer Science 1325.
Cumulative. Closed book.  Bring a calculator.  One 8.5x11 sheet of paper with notes 
on both sides allowed.  Emphasizes topics since the Midterm Examination, including 
readings (chapters 13 except 13.7, 14.1 - 14.4 through the first half of page 509, 
15.1, 15.3, 15.6, 17.6, 17.7, 20.1, 20.2 through page 718, 20.4 - 20.7, pages 845-
846 on agglomerative and k-means clustering) and the required paper on HMMs. The 
two papers on SVMs and Bayesian Networks are highly recommended but not required. 
You are also responsible for material presented in the lectures and lecture notes. 
You should be knowledgeable of the material in the homework assignments assigned 
after the midterm exam. You are responsible for topics covered in lecture even if 
there are no lecture notes on the topic. You should have knowledge sufficient to 
work through simple examples using the algorithms for basic probability, uncertainty 
reasoning, Bayesian networks, Naive Bayes, neural networks, support vector machines, 
perceptron learning, backpropagation, k-nearest-neighbor, clustering, game theory, 
and speech recognition. The exam will focus on material since the midterm. Questions 
may, however, refer back to issues brought up with search, logic and decision trees, 
for example, so you should refresh your memories about the main ideas and methods 
from the material associated with the midterm examination. For example, you should 
be able to relate general search questions to the topics in this part of the course 
(e.g., what is the search space and what is the search method).  Guest lectures 
will not be covered in the exam.

Exam grading questions must be raised with the instructor within one week after it is returned.

Academic Integrity: 

All examinations, programming assignments, and written homeworks must be done
individually.  Cheating and plagiarism will be dealt with in accordance with
University procedures (see the UW-Madison Academic Misconduct Rules and Procedures).
Hence, for example, code for programming assignments must not be developed in
groups, nor should code be shared.  You are encouraged to discuss with your peers,
the TA or the instructors ideas, approaches and techniques broadly, but not
at a level of detail where specific implementation issues are described by anyone.
If you have any questions on this, ask the instructor before you act.

Exam Archives: 

(Note the exam format, scope and order of topics might be different.)
  Fall 09 midterm | Answers
  Fall 08 final
  Fall 08 midterm
  Fall 06 final
  Fall 06 midterm | Answers
  Fall 05 final
  Fall 05 midterm | Answers
  Prof. Dyer's CS540 exam archives

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