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CS 540 - Introduction to Artificial Intelligence
Section 1
Spring 2013
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CS 540 Examinations
Schedule
- Midterm Examination
Tuesday, March 12, 7:15 p.m. - 9:15 p.m., room 113 Psychology
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 - 6.4),
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.
- Final Examination
Tuesday, May 14, 12:25 p.m. - 2:25 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, 18.10 and 23.5)
and the required paper on
HMMs.
The papers on Bayesian Networks and Eigenfaces are 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, random forests, 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,
Adaboost, Viola-Jones face detection algorithm, 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).
- Solution
- Histogram of Scores
- Average score: 76.6
- Median score: 79
- 90th percentile: 92.7, 80th percentile: 91, 70th percentile: 89, 60th percentile: 84.6, 50th percentile: 79, 40th percentile: 74, 30th percentile: 69, 20th percentile: 66
Old Exams
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