CS 540: Intro to AI, University of Wisconsin

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General Info

Grades (5/21)

CS 540 Examinations

Schedule

Midterm Examination
Wednesday, March 14, 7:15 p.m. - 9:15 p.m., room 1221 CS
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 Search (Chapters 3.1 - 3.6, 4.1, 5.1 - 5.3, and 6.1 - 6.3), and some Machine Learning (Chapters 18.1, 18.2, 18.6.3, 18.6.4, 18.7, and 18.9) 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. 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. A summary of topics is given HERE.
- Solution
- Average score: 85.7
- Median score: 88
- 90th percentile: 94.6, 80th percentile: 93.0, 70th percentile: 91.0, 60th percentile: 89.0, 50th percentile: 88.0, 40th percentile: 86.0, 30th percentile: 84.2, 20th percentile: 79.8
- Histogram of Scores
- Approximate correspondence between numeric scores and letter grades:
```
	 92-100    A
	 88-91     AB
	 83-87     B
	 79-82     BC
	 69-78     C
	 60-68     D
	 0-59      F
	 
```
Final Examination
Wednesday, May 16, 2:45 p.m. - 4:45 p.m., room 168 Noland
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, lectures and assignments. That is, covers readings (Russell and Norvig chapters 7.1, 7.3 - 7.5, 8.1 - 8.3, 13, 14.1, 14.2, 14.4, 15.1 - 15.3, 18.3, 18.4, and 23.5) and the required papers on HMMs and Eigenfaces. The paper on Bayesian Networks is recommended but not required. 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 covered in class including decision trees, entropy, information gain, Ockham's razor, overfitting, k-fold cross-validation, leave-one-out cross-validation, basic probability, uncertainty reasoning, full joint probability distribution, marginalization, summing out, conditioning rule, product rule, chain rule, conditionalized version of chain rule, Bayes's rule, conditionalized version of Bayes's rule, independence, conditional independence, Bayesian networks, inference by enumeration, Naive Bayes, Markov model, Hidden Markov model, speech recognition, language model, acoustic model, face recognition using eigenfaces, propositional logic, interpretation, model, entailment, valid, tautology, unsatisfiable, contradiction, soundness, completeness, inference rules, modus ponens, resolution rule of inference, monotonicity property, deductive inference by enumeration (truth table construction, model checking), natural deduction algorithm, resolution refutation algorithm, conjunctive normal form, clause, Horn clause, literal, forward chaining, backward chaining, generalized modus ponens rule of inference, first-order logic, universal quantification, existential quantification. A summary of topics is given HERE.
The exam will focus on material since the midterm. Questions may, however, refer back to issues brought up before the midterm, however, so you should refresh your memory 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