CS 540: Intro to AI, University of Wisconsin

CS 540 Examinations

Schedule

Midterm Examination
Wednesday, October 21, 7:15 p.m. - 9:15 p.m., Room B130 Van Vleck
Closed book. Bring a calculator. One 8.5 x 11 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 - 4.3 (except "Memory-bounded heuristic search," pages 101-104), 5.1, 5.2, and 6.1 - 6.3), and Logic (Chapters 7.1, 7.3 - 7.5). The following readings were assigned but will not be covered in the exam: Chapters 1 and 2. You are responsible for topics covered in lecture even if there are no lecture notes on the topic (e.g., constraint satisfaction methods). Also, 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.
- Solution
- Average score: 78
- Median score: 80
- Approximate correspondence between numeric scores and letter grades:
```
	 90-100    A
	 85-89     AB
	 78-84     B
	 70-77     BC
	 62-69     C
	 55-61     D
	 0-54      F
	 
```
Final Examination
Tuesday, December 22, 5:05 p.m. - 7:05 p.m., Room 1221 CS
Closed book. Not cumulative -- covers topics since the midterm exam though will also refer back to basic concepts from the first half of the course. One 8.5 x 11 sheet of paper with notes on both sides allowed. Bring a calculator.
Emphasizes topics since the Midterm Examination, including readings (Russell and Norvig chapters 10.3 through page 334, 11.1 - 11.4, 13 except 13.7, 14.1, 14.2, 14.4 through the first half of page 509, 15.1, 15.3, 15.6, and 20.5 - 20.7) and the two required papers on HMMs and eigenfaces. 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 situation calculus, STRIPS language, STRIPS algorithm, POP planning, basic probability, uncertainty reasoning, Bayesian networks, Naive Bayes, neural networks, support vector machines, perceptron learning, backpropagation, face recognition using eigenfaces, speech recognition, etc. 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).

Old Exams

CS 540 | Department of Computer Sciences | University of Wisconsin - Madison

CS 540 - Introduction to Artificial Intelligence

CS 540 Examinations

Schedule

Old Exams