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ICS 171: Introduction to Artificial Intelligence– Fall 2005

Note Board: - Last class cancelled.

ICS 171 Max Welling

Organization Policies Topics Homeworks Slides Grading

1. Organization (back to top)

I&C Sci 171 INTRO ARTIFCL INTEL

Code

Typ

Sec

Unt

Instructor

Time

Place

Max

Enr

Req

Nor

Rstr

Ead

Web

Status

36470

Lec

WELLING, M.

TuTh 2:00- 3:20p

CS 174

100

125

Ead

OPEN

36472

Dis

WELLING, M.

W 10:00-10:50

SST 220B

Ead

OPEN

36473

Dis

WELLING, M.

W 3:00- 3:50p

ET 204

Ead

OPEN

Instructions begins on Tuesday, Sept.27

Instructor: Max Welling, welling@ics.uci.edu
Office hours: Fr. Noon-1pm in CS 414C

Teaching Assistant: Radu Marinescu radum@ics.uci.edu
Office hours: Tu.Th. 4pm-5pm in CS/E 331.

Readers: Matthew Johnson johnsonm@uci.edu
Nick Noack nnoack@ics.uci.edu

2. Policies (back to top)

Text:

There is one required textbook for the course:

Artificial Intelligence, A Modern Approach.
Second Edition, by Russel and Norvig (Prentice Hall).

The textbook explains the subject material in detail. It is strongly recommended that you read the book. It is strongly recommended that you read the book and attend all lectures and all meetings of your discussion section. You will be responsible for all material covered in the lectures and discussion sections, and for all assigned reading in the book.

Grading:

-Two quizzes (10%)
-Two projects (20%)
-A midterm (30%)
-A Final Exam (40%)

These are guidelines intended to help students plan their work in this course.
However, the instructor does reserve the right to make changes in these evaluation criteria.
A work-related conflict is NOT a valid reason for postponing an examination.
Please bring your student ID to all examinations.

Graded Quizzes and Assignments:

These can be picked up from Distribution Center or in Discussion Section

Grading Disputes:

Turn in your work for regrading at the discussion section to the TA within 1 week.
Note: we will regrade the entire paper: so your new grade could be higher or lower.

Questions on Grading:
For any questions regarding grades, please contact the TA during his office hours or at the end of the discussion sections.

Obtaining Assistance:
The best way to get your questions answered is by coming to lecture, discussion or office hours and asking them there.

Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

Announcements:
Class announcements will be made in lecture and in discussion. Important announcements will also be posted on this class Web page.
The Web page will contain the most up-to-date course information.
Any corrections or additional explanations for the homework assignments will also be posted there,
so please check the Web page occasionally to stay up to date.

Homework and Handouts:
The homework assignments are regularly posted on the Web.

Some homework problems may be difficult. Homework that is turned in should be legible and well-written.
A badly written, poorly presented solution to a problem is of little value even if it happens to be correct.

The homework problems are an integral part of the course. They complement the material covered in the lectures by providing examples, applications, and extensions. You are strongly encouraged to attempt all problems. Even if you cannot solve them, if you have tried hard to solve them you may be more likely to understand and remember the solution. Our brains learn something while attempting to solve a problem, even (and perhaps especially) during failed attempts. So do not get discouraged if a problem is difficult.

Discussion Section:
You must be registered for a discussion section. The discussion section provides you with an opportunity to ask questions about the lecture material. It is strongly recommended that you attend a discussion section regularly. You are responsible for all material covered there.

Academic Honesty:
All work done on quizzes, midterms and finals should be your own work. Cheating on any kind of in class examination will be taken very seriously.
Any such incident will result in a letter describing the incident which is placed in your file on campus.
Depending on the severity of the incident, the resulting grade can range from an F on the particular
examination to an automatic F in the course. Additional penalties may also be imposed by the department and the university.
Very severe incidents of academic dishonesty can result in suspension or expulsion from the university.

ICS Change of Grade Option Policy:
The ICS departmental deadline for any ICS major to change their grade option is the end of 6th week with instructor's approval. Dean's signature (available at the ICS Student Affairs office) will be required after the deadline and the ICS Student Affairs office does not allow a change of grade option for any course after 6th week, unless the student has documented a medical or financial hardship.

ICS Add Deadline:
The ICS departmental deadline for any ICS major to add an ICS course is the end of 3rd week with instructor's approval. Any course additions after the 3rd week of classes requires Dean's signature and careful review by the ICS Student Affairs office. If you are adding a course after the 3rd week, please go to the ICS Student Affairs office.

4. List of Topics (back to top)

The following represents a very preliminary syllabus. Expect significant changes.

Lecture 1. Introduction: Goals, history (Ch.1)

Lecture 2. Agents (Ch.2)

Lecture 3-4. Uninformed Search (Ch.3)

Lecture 5-6 Informed Search (Ch.4 NOT sec.4.5 and after)

Lecture 7-8. Constraint satisfaction (Ch.5).

Lecture 9-10 Games (Ch.6)

Lecture 11. Midterm

Lecture 12. Propositional Logic (Ch.7 NOT “circuit based agents” on page 227 and after)

Lecture 13. First Order Logic (Ch.8 NOT sec. 8.4 and after)

Lecture 14. Statistical Learning Methods
(Ch.20 ONLY sec. 20.2 pages 716,717,718 and sec. 20.4 pages 733,734,735 until kernel methods and pages 740,741,742,743)

Lecture 15-16 . Learning (Ch.18 NOT including sec.18.5 and after).

Lecture 17Uncertainty (Ch.13)

Lecture 18. Thanksgiving

Lecture 19-20. Inference in first order logic (Ch.9 ONLY sec. 9.1)

5. Homeworks (back to top)

Although we will not actually grade your homework, it is a good idea to turn it
in so we can correct it.

week 1: Homework 1

week2: Homework 2

week3: Homework 3

week4: Homework 4 Project 1 (due Nov.10)

week5: Homework 5

week6: Project 2 (due Dec.8) training data attributes training data labels testing attributes testing labels (tab delimited ASCII format)