CS 1573: Artificial Intelligence Application Development (Spring 2004)

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:

  • Ability to build simple versions of a few AI applications

  • Familiarity with full-scale versions of the same applications

  • Understanding of AI programming paradigms

  • Mastery of some AI toolkits for AI applications and rapid prototyping

    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!)

    Syllabus (evolving and subject to change!):

    Class Topic Reading Assignments
    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)

    Submission Instructions


    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)

    Program 2 (Evaluation) Update


    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    
    2/19, 2/26 Introduction to Machine Learning Handout

    Program 3 (dialogue) assigned (due 3/18)

    Evaluation Data

    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)

    grading sheet

    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

    Quiz 6

    Program 5 (due 4/15)

    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  


    A Turing Test cartoon

    Weizenbaum and Eliza

    Artificial Intelligence:

    Books on Reserve:

    • Artificial Intelligence: Theory and Practice, by Dean, Allen, and Aloimonos
    • Reinforcement Learning: An Introduction, by Sutton and Barto
    • Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, by Jurafsky and Martin
    • Empirical Methods for Artificial Intelligence, by Cohen

    Academic Integrity:

    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.


    Some of the materials used in this course borrow from the courses of Russell & Norvig, Henry Kautz, and Andrew Barto.

    Previous versions of this course:

  • Spring 2003

    Students With Disabilities:

    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.