CS 1573: Artificial Intelligence Application Development (Spring 2004) |
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| Time: | Tu Th 2:30-3:45 | Place | 6516 Sennott Square (NOTE ROOM CHANGE!!) |
| Professor: | Diane Litman | Office Hours: | M 10-12 (741 LRDC), Tu Th 3:45-4:45 (5105 Sennott Square) |
| Email: | litman@cs.pitt.edu | Phone: | 412-624-8838 (Sennott Square); 412-624-1261 (LRDC) |
| TA: | Beatriz Maeireizo | Office Hours | 5420 Sennott Square, see homepage for hours |
| Email: | beamt@cs.pitt.edu | Phone: | 412-624-8462 |
This course will focus on the development of artificial intelligence applications. We will examine current research in artificial intelligence, with an emphasis on algorithms and representations that can be put to use in solving practical problems now or in the near future. Multiple areas of artificial intelligence will be covered, with a focus on topics not covered during CS 1571. Course objectives include:
PREREQUISITES: CS 1571, or consent of the instructor
Artificial Intelligence: A Modern Approach (2nd edition), by Russell and Norvig
We will also be using readings from other AI textbooks, online toolkits (e.g., WEKA), and other resources.
GRADE BASIS: assignments (45%), midterm exam (15%), final exam (15%), project (15%), class participation / pop quizzes (10%)
ABSENCES AND LATE ASSIGNMENTS: If an absence is unavoidable, you are still responsible for making arrangements to turn in the assignments on time. You are also responsible for obtaining any materials passed out and the information announced during the missed class. In case of extraordinary circumstances (hospitalization, family emergency) you should contact me as soon as possible so that we may arrange an extension for assignments prior to the due date. In all other cases, if an assignment can be accepted late, the penalty is 10% per day up to 5 days including Saturday, Sunday, and holidays. Assignments are due at the start of class. There are NO makeup possibilities for exams.
Grades (raw scores, you can get the course grades from the Pitt website)
Friday February 20: come to lunch and to a special ISP seminar on machine learning (Speaker: Russ Greiner Department of Computing Science and Alberta Ingenuity Centre for Machine Learning, University of Alberta; Title: Budgeted Learning of Naive-Bayes Classifiers) (12:00, 5317 Sennott Square)
CNN article on Robot Scientists (thanks DS!)
| Class | Topic | Reading | Assignments | |
| PART 1: BACKGROUND | ||||
| 1/6 | Course Overview and Administration | |||
| 1/8 | Introduction to Artificial Intelligence | Chapter 1, AIMA | ||
| 1/13, 1/15, 1/20 | Intelligent Agents; Empirical Methods |
Chapter 2, AIMA Empirical Methods Handout |
Quiz (1/15); answers
Program 1 (due 1/29) |
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| Part 2: NATURAL LANGUAGE PROCESSING | ||||
| 1/20, 1/22 | Introduction to Natural Language Processing | Chapter 1, Speech and Language Processing, by Jurafsky and Martin | ||
| 1/22, 1/27, 1/29 | Regular Expressions | Handout | Quiz (1/27); answers Program 2 (due 2/10, 2/19) |
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| 1/29, 2/3, 2/5 | Communication | Chapter 22, AIMA (but skip pages 800-805, 808-818, 824-826) | ||
| 2/5, 2/10, 2/12, 2/17 | Dialogue Agents | Handout (skip 19.4) | Quiz (2/10) | |
| 2/17 | Review Session | |||
| PART 3: MACHINE LEARNING | ||||
| 2/19, 2/26 | Introduction to Machine Learning | Handout | Program 3 (dialogue) assigned (due 3/18) |
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| 2/24 | MIDTERM | Covers Parts 1 and 2 | ||
| 2/26, 3/2, 3/16, 3/18 | Learning from Observations (ps, pdf) | Chapter 18, AIMA | Quiz 4 and answers Project Assigned (3/2) Program 4 (due 4/6) |
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| 3/18, 3/23, 3/25, 4/1, 4/6, 4/8 | Reinforcement Learning | Chapters 1-3, Reinforcement Learning: An Introduction, by Sutton and Barto | Quiz 5 and answers Program 5 (due 4/15) |
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| 4/13 | Review Session | |||
| 4/15 (class will be longer today) | PROJECT PRESENTATIONS | Presentations (5-10 min) | Project (Report) Due: FOLLOW HW SUBMISSION PROCEDURES | |
| April 22 - 2:30 (NOTE CHANGE!!!!!) | FINAL (non-cumulative) | Covers Part 3 | ||
Artificial Intelligence:
Assignments must be your own individual work, unless explicitly stated otherwise. You must do the work without undue help from other people, and you must not present material from resources such as the Web, books, papers, code listings, and other people as your own. You may talk to each other about concepts and techniques, but you must not discuss specific solutions or approaches to solutions. Copying or paraphrasing someone's work, or permitting your own work to be copied or paraphrased, even in part, is not allowed and will result in an automatic grade of 0 for the assignment.
If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services, 216 William Pitt Union, (412) 648-7890/(412) 383-7355 (TTY), as early as possible in the term. DRS will verify your disability and determine reasonable accomodations for this course.