CSE352
ARTIFICIAL INTELLIGENCE
SUMMER 2009


Course Information


News:

  • Please bring corrected homework #2, part 2 to class on this Thursday(July 2).
  • There are a NEW Homework #3 and a NEW homework #4 in Downloads. All students should solve these ones, not old ones.
  • NEW Syllabus is in Downloads.
  • NEW homework schedule is posted below.
  • Project Tool link is listed below.
  • We start Machine Learning part (new hmk2, parts 1,2) on Tuesday, June 9
  • Lecture Notes: AI Introduction, Chapter 2, Introduction to Learning, Preprocessing, and Supervised Learning: Classification are Downloads

    • Time:

    Tuesday, Thursday, 6:00pm - 9:25 pm

    Place:

    Humanities 2047

    Professor:

    Anita Wasilewska

    1428 CS Building; 632-8458
    e-mail: anita@cs.sunysb.edu
    Office Hours: Tuesday, Thursday at 5:00 - 5:45 pm, and by appointments.

    Teaching Assistant:

    Raksik Kim
    e-mail: raksik@gmail.com
    Office Hours: tba

    Book:

    The Essence of Artificial Intelligence
    Allison Cawsey
    Prentice Hall, 1998

    General Course Description:

    Artifficial Intelligence is a broad and well established field. The AI textbooks seem to be getting longer and longer. Our cource textbook attemts to reverse this trend. It provides a concise and accessible introductionto the field.
    The course will closely follow the book and is designed to give a broad, yet in-depth overview of different fields of AI. It will examine the most recognized techniques in a more rigorous detail. For this part we will provide detailed lecture notes distributed in class and available on the web. It also will explore the newest trends and developments of the field in form of students talks based on newest research and applications from the field.

    Student Information

  • Students ATTENDENCE is essential for the course as students presentations are integral and as important part of the course design as Professor's lecture.

    • Project Data

  • Play around with the data and familiarize yourself with it (DOWNLOAD: bakarydata.xls )

    • Project Tool

    WEKA Machine Learning Project TOOL

    Students Input

  • Here are the links for the Loebner Prize.
    Please send me more, if you find some interesting pages, or articles.

    • Loebner Prize Main Page
    Minsky Comments
    2005 Contest
    2005 Winner (Jabberwacky)

    Presenations schedule

  • PRESENTATION PROPOSAL is due Tuesday June 9
  • Presentations 1: Titles: t.b.a, Thursday, June 11
  •       1.Johnny Wang, Title: Video Analytics - Slide Download

  • Presentations 2 Titles: t.b.a, Thursday, June 18
  •       1. Rene Camacho, Title: the winner of the DARPA Grand Challenge, "Stanley the robot" - Slide Download
  •       2. Kristopher Thomson, Title: Natural Language Processing - Slide Download
  •       3. Jakub Pezacki, Title: Semantic web and applications - Slide Download

  • Presentations 3: Titles: t.b.a, Thursday, June 25
  •       1. Brian Horn, Title: Swarm Intelligence - Slide Download
  •       2. Nephtal Rodriguez, Title: Massively Multiplayer Online Game Bots - Slide Download
  •       3. Chunfung Lee, Title: AI of poker game - Slide Download
  •       4. Liang Liang, Title: Roomba the Vacuum Cleaning Robot - Slide Download

  • Presentations 4: Titles: t.b.a, Thursday, July 2
  •       1. David Lu, Title: Voice recognition - Slide Download
  •       2. Christopher Triolo, Title: Computer Vision - Slide Download
  •       3. Jin Kang, Title: "Ai research from www.a-i.com"
  •       4. Bin Zhou, Title: Application in people's life - Slide Download

  • Presentations 5: Titles: t.b.a, Thursday, July 7 - There can be only ONE student.
  •       1. Ferose Babu, Title: AI in Game Programming

  • PROJECT PRESENTATIONS, July 9

    Homeworks and Tests Schedule

  • Homework 1 due Thursday, June 11
  • Homework 2, part 1 due Tuesday, June 16
  • Homework 2, part 2 due Thursday, June 18
  • Homework 3 due Tuesday, June 23
  • Homework 4, part 1 due Thursday, June 25
  • Homework 4, part 2 due Tuesday, July 2
  • Project Homework (YOUR PROJECT DATA) due Thesday, June 30
  • FINAL due THURSDAY, July 9, or any day before.

    • DOWNLOADS

    SUMMER 2009 SYLLABUS
    PROJECT DESCRIPTION SLIDES
    Homework 1
    Homework 1 Solution
    Homework 2, Part 1
    Homework 2, Part 2
    Homework 3
    Homework 3 Solution
    Homework 4, part 1
    Homework 4, part 2
    TAKE HOME FINAL

    Past Courses Students Presentations

    Natural Language Processing
    Deep Blue
    Fuzzy Logic and its Applications
    Going To See The Wizard
    Autonomous Vehicles
    AI in Computer Vision; Past, Present and Future
    AI in Chess Playing
    Genetic Algorithms
    Computer Vision and Facial Recognision
    PROJECT Presentation Example 1
    PROJECT Presentation Example 2

    Lecture Notes:

    Chapter 1; Introduction to AI
    Chapter 2; Knowledge Representation
    Chapter 2; Expert Systems, Handout 1
    Chapter 2; Semantic Nets
    Chapter 2; Predicate Logic 1
    Chapter 2; Predicate Logic 2
    Chapter 2; Predicate Logic 3
    Chapter 2; Predicate Logic 4
    Propositional Resolution 1
    Propositional Resolution 2
    Propositional Resolution 3
    Introduction to Learning
    Classification-Supervised Learning, Part 1
    Classification-Supervised Learning, Part 2
    Classification by Decision Tree, Part 3
    Decision Tree, Special Majority Voting Examples
    Classification - Testing, Part 4
    Data Preprocessing
    Classification By Neural Networks (optional)
    Bayesian Classification (optional)
    Genetic Algorithms(optional)

    DATASETS

    Datasets for learning, data mining and knowledge discovery
    University California Irvine KDD Archive
    World Bank datasets
  • Please email and discuss with the Professor the subject of your presentation as soon as possible.