ICS 171: An Introduction to
Artificial Intelligence Winter Quarter, 2001
Learn the basic AI techniques, the problems to which they are applicable and their limitations. Topics covered include search algorithms (including heuristic search), knowledge representation, learning algorithms, and elements of probabilistic modeling.
Academic honesty is taken very seriously. It is the responsibility of each student to be familiar with UCI's current academic honesty policies. Please take the time to read the current UCI Senate Academic Honesty Policies. Also you may want to look at the ICS Department's policies on cheating .
There will be 4 (four) biweekly homework assignments that are intended to help you understand material and keep up with what is covered on the lectures. Homeworks will account for 10% of your score. TAs will only grade a randomly chosen fraction of problems in each homework, to ensure that you get full credit you will need to solve all the problems. To motivate solving the homeworks I promise to give at least one problem from the homework assignment on the quiz, possibly with minor changes. Solutions to both homeworks and quizzes will be discussed in great detail at the discussion sections. Homeworks are due on Mondays (please, check the syllabus below) in the class before the quiz. The homeworks turned in the same day during or immediately after the lecture will loose 25% of the grade, no late homeworks are accepted after the instructor left the lecture hall.
There will be 4 (four) 20-25 minute biweekly quizzes. Quizzes will be administered on the same Mondays that the homeworks are due (please, check the syllabus below) and will cover the same material as the homeworks. We will drop one and only one quiz with the lowest score. The quizzes will account for 30% of your overall grade.
There will be a closed-book midterm exam. This will account for 30% of your grade.
There will be a closed-book final exam, during the finals week. This will account for 30% of your grade.
Policy on Regrading: Turn in the paper, with a reason for the request for regrading written down on a separate sheet of paper and a signed statement that the paper wasn't altered in any way, to your TA within 1 week of receiving it. Note that the entire paper will be regraded which could result in your grade either increasing or decreasing.
Feel free to ask questions by either sending an e-mail to 171-questions@ics.uci.edu and/or to the ics.171 news group . The e-mail sent to 171-questions@ics.uci.edu will be automatically duplicated to the TAs and the instructor, increasing the likelihood of the prompt response. Also please check this news group ics.171 for announcements, answers to homework etc. If you don't understand something, others probably don't either and will have the same question.
Note that some changes in the lecture sequence and/or timing may occur due
to a number of reasons including but not limited to the discretion of
instructor.
|
Lecture #, Date |
Lecture Topic, Recommended Reading |
Quiz / Exam |
Homework |
Lecture
Notes |
|
L1, |
Course
Introduction, What is AI? |
|
|
|
|
L2, Wed,
January 10 |
Statement
of Search Problems in AI. Examples. Uninformed Search |
|
|
|
|
L3, Mon,
January 15 |
Martin Luther King, Jr. Birthday Holiday. No Class. |
|
|
|
|
L4, Wed,
January 17 |
Informed
Search |
|
|
|
|
L5, Mon,
January 22 |
Optimal
Search, A* algorithm and its properties. |
QUIZ |
HWK 1 due. |
|
|
L6, Wed,
January 24 |
IDA*;
Optimization as a Special Type of Search. |
|
||
|
L7, Mon,
January 29 |
Game
playing. |
|
|
|
|
L8, Wed,
January 31 |
Game
playing (ctd.) Minimax Search and a -b
pruning. |
|
|
|
|
L9, Mon,
February 5 |
Knowledge
Representation. Propositional Logic. Syntax. Semantics. Inference Rules. |
QUIZ |
HWK2 is due. |
|
|
L10, Wed,
February 7 |
Propositional
Logic. Inference Rules. |
|
Sample midterm and HWK 3 are available |
Solutions to Sample Midterm (Postscript) and HWK 2 Solutions (MS Word) |
|
L11, Mon,
February 12 |
Knowledge
Representation. First order (Predicate) Logic. |
|
|
|
|
L12, Wed,
February 14 |
MIDTERM EXAM |
|
|
Feb. 16 -LAST DAY TO DROP THE COURSE |
|
L13, Mon,
February 19 |
President's Day Holiday. No Class. |
|
|
|
|
L14, Wed,
February 21 |
Resolution
as a Complete Inference Procedure. Examples. Review of Logic. |
|
|
Note Set 7: Inference in FOL, Resolution proofs (Postscript) |
|
L15, Mon,
February 26 |
Reasoning
Under Uncertainty. Rules of Probability. Bayes Rule. Optimal Decision Making. |
QUIZ |
HWK 3 is due |
Note Set 8: Uncertainty and optimal decision making (Powerpoint) |
|
L16, Wed,
February 28 |
Machine
Learning. Types of Learning. Examples. Probabilistic Classification. Naïve
Bayes Classifier. R.&N. Ch. 18 (18.1-18.4) R.&N;.
Ch. 14 (14.4) |
|
||
|
L17, Mon, March
5 |
Perceptron
and Neural Networks. |
|
|
Note Set 9: Bayes rule, probabilistic classification (Powerpoint) |
|
L18, Wed, March
7 |
Learning
Decision Trees. |
|
|
|
|
L19, Mon, March
12 |
Clustering.
Statement of the Problem. Hierarchical and K-means clustering |
QUIZ |
HWK 4 is due. |
|
|
L20, Wed, March
14 |
REVIEW |
|
|
|
|
|
FINAL EXAM according to the schedule of classes |
|
|
|
Essays and Papers
Websites
Much of the course material used has been developed by others, and I would like to thank Padhraic Smyth, Stephen Bay, and the textbook authors for making available their notes.
Homepage Advice Page My Work at Yahoo! Page