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CS 540 - Introduction to Artificial Intelligence
Section 1
Fall 2019
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CS 540 Examinations
Schedule
- Midterm Examination
Thursday, October 24, 7:15 p.m. - 9:15 p.m.
Students with last names starting with A - Lin will take the exam in room B130 Van Vleck
Students with last names starting with Liou - Z will take the exam in room 3650 Humanities
All questions will be True/False and multiple choice. Closed book.
Bring your student ID number, a pencil, an eraser.
Bring a calculator, if possible. No phones allowed.
One 8.5 x 11 sheet of paper with (typed or handwritten) notes on both sides allowed.
Covers topics in first half of the course up through decision trees
and k-nearest-neighbors, including
readings,
lectures
and
assignments.
That is, covers
Search (Chapters 3.1 - 3.6, 4.1 (except 4.1.4), 5.1 - 5.3, 5.5),
and some Machine Learning (Chapters 18.1 - 18.4, 18.8.1).
It will not cover Genetic Algorithms or Mean Shift Clustering.
The following readings were assigned but will not be covered in
the exam: Chapters 1, 2.
You are responsible for topics covered in lecture even if there
are no readings associated with a 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.
- Solutions
- Average score: 82.9
- 90th percentile: 96, 80th percentile: 93, 70th percentile: 91, 60th percentile: 88, 50th percentile: 86, 40th percentile: 83, 30th percentile: 78, 20th percentile: 73
- Final Examination
Tuesday, December 17, 12:25 p.m. - 2:25 p.m.
Students with last name starting with A - F in room 1111 Humanities
Students with last name starting with G - Z in room 3650 Humanities
All questions will be True/False and multiple choice. Closed book.
Bring your student ID number, a pencil, and an eraser.
Bring a calculator, if possible. No phones allowed.
One 8.5 x 11 sheet of paper with (typed or handwritten) notes
on both sides allowed.
Not cumulative -- covers topics since the midterm exam though questions may
refer back to basic concepts from the first half of the course.
Emphasizes topics since the Midterm Examination including
readings,
lectures
and
assignments.
That is, covers readings (Chapters
6.1 - 6.4,
13, 14.1, 14.2, 14.4, 15.1 - 15.3, 18.6.3, 18.6.4, 18.7, 18.9, 18.10, 23.5)
and the two required papers on deep learning and HMMs.
The paper on Bayesian Networks is recommended but not required.
You should have knowledge sufficient
to work through simple examples using the algorithms covered in class
including
constraint satisfaction problems, neural networks, Perceptrons, backpropagation, deep learning, convolutional neural networks,
support vector machines, support vectors, margin, slack variables, kernel trick,
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, weak classifier, decision stump, weighted-majority classification, boosting ensemble learning, Viola-Jones face detection algorithm.
Nothing on the Forward algorithm, the Viterbi algorithm, or the Forward-Backward algorithm for HMMs. Also, nothing on Siri, particle filters, or tracking in video.
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
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