EECS 492: Introduction to Artificial Intelligence Winter 2001

This page is where information about the class will be distributed.  You should check here regularly for updates to the schedule, important announcements, and other items of interest. You are responsible for being aware of the material on this page.

Contents

  1. New Announcements
  2. General Information
  3. Contact Information
  4. Course Objectives
  5. Lectures and Discussion Sections
  6. Textbook
  7. Prerequisites
  8. Course Material Online
  9. Assignments
  10. Grading
  11. Exams
  12. Lecture and Homework Schedule
  13. Visit the Wumpus World!
  14. Play Rock-Paper-Scissors

New Announcements

April 22: The final exam is tomorrow, April 23rd, at 1:30pm.  It will be in the usual exam rooms: 1200 and 1005.  Usual format (except 2 hours long).

April 22: As announced in class last Tuesday, solutions for PS6 are available through the assignment itself.

Apr 6: PS5s are graded.  Sergej will have them at discussion and office hours today, and they’ll be at class on Tuesday.  Average was 75 (but median was in the low 80s; the tail (in part due to late penalties) was long.) Some comments from the grader:

Task 1 (10pts): I gave people partial credit for a hand-waving but correct answer and full credit for an actual (correct) proof.

Task 2 (30pts): A lot of people really had problems here. Common problems were not using the cutset method in 2.1, and getting pretty confused by 2.4 and 2.5. Typically, errors in the beginning propagated through, so I tried to follow people's reasoning and give partial credit for doing the right thing with the wrong numbers.

Task 3 (20pts): A mixed bag here. A lot of people assumed the phenotypes were equally proportional in task 3.2, which is not valid. Many people had problems with 3.3, esp. the first two parts. When P(F) is 1/3, they could work it out, but they got confused when p(jedi) = 0.0001, even though they're basically the same problem.

Task 4 (15pts): Most people did fine - a few folks are a little fuzzy on explaining away.

Task 5 (25pts): Common problems were forgetting to use the priors from task 3 or using phenotypes as the variable values. Again, I tried to give partial credit where possible.

Apr 3: Solutions to the second midterm are here.  The mean was about a 65.

Apr 2: The version space example from class is in the handouts directory (here).

Mar 23: Midterm 2 is next Tuesday, starting at 9am (not 9:10)!!  Same format as MT1; you can bring a 1-page cheat sheet, and a calculator might be handy.  I have reserved 1005 EECS as an overflow room.

Mar 23: Solutions to PS5 can be found here.

Mar 22: PS4 was handed back today.  Solutions will be linked through the assignment as soon as possible.  The average on the assignment was ~76% .

Mar 13: Prof. Durfee’s office hours on Wed 3/14 will start late, at roughly 10am.  If you need to set up a separate appointment, send him email.

Mar 8: PS3 was handed back today.  Mean was 76.3; median was 80.

Grader comments: In general, it seems like there's some folks who need to start working on these homeworks sooner & putting a little more effort into it. Quite a few people didn't do the last couple of questions, which hurt their grade quite a bit.

The first four tasks were each worth 10 points. Most folks did fine on these, although they may have lost a few points for skipping steps.

The last four questions were worth 15 points each.

Task 5 is where it started to go downhill for a lot of folks - most people lost significant points on this one.

Task 6 was also a problem - people lost points for not doing the CNF conversion properly or not giving enough detail in their proofs. (e.g., just writing a series of sentences & not saying how they got from one to the next.) Some folks also gave some informal English-language proofs, which were not helpful.

Task 7 most folks did OK on - a lot of people lost a few points for not listing all the answers to who was afraid of Phobos.

Task 8, again, most people did OK, although quite a few people lost some points for not doing a refutation proof. Reading the assignment carefully would have helped them.

Feb 20: Solutions to the midterm will be placed here.  The mean was about a 70.

Feb 10: Examples from class are here.

Feb 8: Remember that the first midterm is on February 15th, starting promptly at 9am!

Feb 8: Remember that PS3 cannot be turned in late.

Jan 30: Professor Durfee will be out of town the rest of this week.  He will not hold office hours on January 31.  Sergej will give the lecture on February 1.

Jan 30: The second assignment is due today.  The third will be on the web shortly.

Jan 16: The second assignment (PS2) is on the web, and is due on January 30th.

Jan 16: To my knowledge, override requests have been processed. Students wishing to enroll should drop from the waitlist and CRISP into the course.

Jan 8: Remember, the first assignment is due on January 16th.

Jan 4: Welcome to EECS 492!

General Information

Lectures: Tue-Thu, 9:00-10:30, 1200 EECS [Durfee]

Sections:

(2) Tue 12:30 - 1:30, 3427 EECS [Bartold]

(3) Thu 12:30 - 1:30, 3427 EECS [Bartold]

(4) Fri 1:30-2:30, 3437 EECS [Roytman]

Contact information:

To contact us, email is often best. Include "492" in message subject lines!

Course Staff

To contact the entire course staff, use the email alias: EECS492@umich.edu.

Professor

Edmund H. Durfee

office: 120 ATL

e-mail: durfee@umich.edu

fax: (734) 763-1260 [if you must]

Office hours: 120 ATL, Wednesdays, 9-10:30am, or by appointment

GSIs

Thomas Bartold

Email: tbartold@umich.edu

Office hours: Mondays, 4-7pm, 3rd Floor of the Media Union

 

Sergej Roytman

Email: ftit@umich.edu

Office hours: Fridays, 2:30-4:30pm, 3rd Floor of the Media Union

Course Objectives

The purpose of this course is to introduce the student to the major ideas and techniques of Artificial Intelligence, as well as to develop an appreciation for the engineering issues underlying the design of intelligent computational agents. The successful student will finish the course with specific modeling and analytical skills (e.g. search, logic, probability), knowledge of many of the most important knowledge representation, reasoning, and machine learning schemes, and a general understanding of AI principles and practice. The course will serve to prepare the student for further study of AI, as well as to inform any work involving the design of computer programs for substantial application domains.

Lectures

Although earlier in the morning than many would like, lectures are an important part of this course. While we will generally follow the course text, lecture is an opportunity to present additional examples, to clarify murkier parts of the text, and to cover complementary technical material.  You will be responsible for material covered in lecture, whether or not it aligns exactly with the text.

Discussion Sections

Weekly one-hour discussion sections will focus on developing programming and other problem-solving skills. Students should attend the same section every week, and are encouraged to bring problems (perhaps based on current assignments) to discuss. These smaller groups are designed to be interactive; students are expected to participate in the solution of problems under discussion.

Office Hours

The instructor and the GSIs will have regularly scheduled office hours each week.  You are encouraged to make use of these to discuss aspects of the course including lecture material and the homework problems. In cases where you cannot make office hours, contact the course staff to arrange an appointment; don’t wait until the last minute though!

Prerequisites

EECS 380, or equivalent experience. We assume programming experience and knowledge of programming language concepts, and familiarity with algorithmic concepts such as graph search and computational complexity. We will not hesitate to employ mathematics where appropriate.

Textbook

Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, 1995.

Course Material Online

Course home page

http://ai.eecs.umich.edu/people/durfee/courses/492winter01. The course homepage is the location of official announcements and other information. You should look at it several times a week to remain updated. You are responsible for keeping aware of announcements through this means. You will also find the files needed for assignments through this homepage, as well as course handouts, syllabus updates, and all sorts of other useful stuff.

E-mail directed to the course staff may be reposted to the website (for example, for FAQs for assignments) unless requested otherwise.

Newsgroup

The course has a newsgroup at umich.eecs.class.492 . The newsgroup is for student use; the instructors do not regularly look at the newsgroup, and so information on the newsgroup does not necessarily reflect the opinions of the course staff. For official announcements, the course website is used. However, often you can get an answer to a question by posting a message to the newsgroup, and someone else in the class will provide you with an answer (but do not post code or problem solutions!). To read the newsgroup, use the UNIX program rn, the X-Windows version, xrn, any of a variety of Macintosh newsreaders, or a world-wide web browser. The caenhelp facility has documentation.

Assignments

There will be six homework assignments (problem sets) in this course, put on the web (at http://ai.eecs.umich.edu/people/durfee/courses/492winter01/assignments) and due at approximately two-week intervals. Some of these will involve some programming or other software work. In addition to programming, assignments might also involve mathematical analysis, information gathering (from WWW and elsewhere), and critical thinking. Do not leave problem sets (especially programming tasks) to the last minute, as network servers sometimes go down, and working through the problems usually takes longer than you think (even when you take this fact into account). We give you two weeks for a reason!

Submitting Assignments

All assignments are to be handed in no later than the start of class (9:10 am) on the due date. If you hand your assignment in after the class has begun, it is late, and will be subject to the late policy.  Be sure to get your homework in on time.  Unless otherwise directed in the assignment, you should turn in a hardcopy of the assignment.  You should not put your assignment in an unsecured drop-off point (such as in an instructor’s mail slot in the EECS or ATL building); instead, you should hand your assignment in directly to an instructor (before lecture, at office hours or discussion section, etc.), or in a secure place (e.g., under the professor’s locked office door).  (Warning: Access to the ATL building is limited between 5pm and 8am.)  On the morning the assignment is due, it must be handed in to the instructors.

Collaboration

We expect strict adherence to the Engineering Honor Code in all assignments and exams. All problem sets (home work assignments) are to be completed on your own. You are encouraged to discuss ideas and techniques broadly with other class members, but all written work, whether in scrap or final form, are to be generated by you working alone unless otherwise expressly stated in the homework assignment. You are not allowed to sit together and work out the details of the problems with anyone. You are not allowed to discuss the problem set with previous class members, nor anyone else who has significant knowledge of the details of the problem set. Nor should you compare your written solutions, whether in scrap paper form or your final work product, to other students (and vice versa), material on the web, assignments from previous years, and so forth. You are also not allowed to possess, look at, use, or in anyway derive advantage from the existence of solutions prepared in prior years, whether these solutions were former students' work product or copies of solutions that had been made available by instructors. Violation of this policy is grounds to initiate an action that would be filed with the Dean's office and would come before the College of Engineering's Honor Council. If you find any ambiguity about this policy, it is your responsibility to contact the course staff.

Late Policy

To be considered on time, assignments must be turned in by the start of class (9:10am) on the due date. You are welcome to submit your answers earlier, certainly. Late assignments will be assessed a penalty of 10% of the assignment's available points per day or fraction thereof, up to a maximum of three days after the due date. If necessary due to the course schedule, some assignments might not have the possibility of three late days; in such cases, the assignment will expressly say so. Since we generally provide this routine slack period, we will not grant extensions except under the most extraordinary of circumstances.

Grading

Problem sets 60% (10% each); Exams 40% (midterms 12% each, final 16%).  (These percentages are subject to slight adjustments at the discretion of instructor.)

If you have a problem with the grading on a particular assignment, write a brief (one-paragraph) description of the problem, and hand it with the assignment to the GSI in section for a regrade. Regrade requests must be submitted within one week of when the graded assignment is made available to the student. Later regrade requests will not be accepted.

Exams

There will be three examinations--two midterms and a final.  The midterms will be held during class period; on those class days the expectation is that the exam will begin at 9AM sharp.  Please ensure that you do not miss the exams, and that you arrive promptly for them.

Midterm#1: Thursday, 15 February, 9:00-10:30AM, 12% of grade.

Midterm#2: Tuesday, 27 March, 9:00-10:30AM, 12% of grade.

Final: Monday, 23 April, 1:30-3:30, 16% of grade.

Lecture and Homework Schedule

(Subject to mid-course adjustments). Readings are from Russell&Norvig. PS = Problem Set.

Warning: The week of February 13,15 will be VERY busy.  Be ready!

Date

Topic

Readings

Homework

4 January

Introduction and Overview

Chap 1

PS1 out

9 January

Intelligent Agents and Problem Solving

Chap 2

 

11 January

Problem Solving

Chap 3.0-3.3

 

16 January

Blind Search

Chap 3.4+

PS1 due, PS2 out

18 January

Informed Search

Chap 4.0-4.2

 

23 January

Memory Bounded Search

Chap 4.3+

 

25 January

Reasoning in Propositional Logic

Chap 6.0-6.4

 

30 January

First-Order Logic

Chap 7.0-7.3

PS2 due, PS3 out

1 February

Logical Agents

Chap 6.5+, 7.4+

 

6 February

First-Order Inference

Chap 9

 

8 February

Planning

Chap 11.0-11.5

 

13 February

Partial Order Planning

Chap 11.6+

PS3 due, PS4 out

15 February

Midterm 1

 

Midterm1

20 February

Other Planning Topics

Chap 12

 

22 February

Plan Execution

Chap 13

 

27 February

Spring Break!

 

 

1 March

Spring Break!

 

 

6 March

Uncertainty and Probability

Chap 14

PS4 due, PS5 out

8 March

Bayesian Networks

Chap 15.0-15.3

 

13 March

Probabilistic Reasoning

Chap 15.4+

 

15 March

Decision Making

Chap 16

 

20 March

Machine Learning: Decision Trees

Chap 18.0-18.4

PS5 due

22 March

Machine Learning: Hypothesis Space

Chap 18.5+

 

27 March

Midterm 2

 

Midterm2, PS6 out

29 March

Learning in Neural Networks

Chap 19

 

3 April

Learning Reprise

 

 

5 April

Agent Modeling

Chap 22.1-22.2

 

10 April

Game Tree Search

Chap 5

 

12 April

Language and Communication

Chap 22

 

17 April

Perspective

Chap 27

PS6 due

23 April

Final Exam  1:30-3:30 PM

 

 

 

Author: Ed Durfee email: <durfee@umich.edu>
Last Updated:
1/4/01