CS 627 Artificial Intelligence

  • Instructor: Prof. Sung-Hyuk Cha

  • CRN: 43694

  • Meeting:

  • Textbook: Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 2nd edition.

  • Course Goals:
    To provide a broad introduction to Artificial Intelligence. The course will cover both fundamental concepts such as search and knowledge representation, as well as applied work in areas such as planning and vision. This course is intended for Computer Science students who have never taken an introductory AI course as well as nonspecialists who wish to acquire a general familiarity with AI. Topics include logic and representation in intelligent agent design, reasoning (search, prediction, planning, explanation), and a overview of AI problems in vision, language, learning, robotics, medicine, and computational design. Special emphasis will be made on learning algorithms using Matlab, e.g., neural networks, decision trees, baysian decision theory, etc.

  • Prerequisites: CS 601 Data Structures & Algorithms

  • Lecture Notes: can be accessed using the http://blackboard.pace.edu.
    Blackboard Login Procedures for Registered Students are available here

  • Project: for description, click here.

  • Upcomming Conferences:
    1. AAAI 2004, July 25-29, 2004, San Jose, California
    2. SCI 2004, July 18-21, 2004 - Orlando, Florida

  • Schedule:

    WeekTopic 1Topic 2
    1 (9/10) Ch 1 Introduction  
    2 (9/17) Ch 2 Intelligent Agents Bayes Decision Theory
    3 (9/24) Ch 3 Solving Problems by Searching Lang Classifier and NBC
    4 (10/1) Ch 3 Solving Problems by Searching Nearest Neighbor
    5 (10/8) more on Searching Decision Tree, ch 18
    6 (10/15) Ch 4 Informed Search and Exploration Artificial Neural Networks, ch 19
    7 (10/22) Ch 4 Informed Search and Exploration Artificial Neural Networks
    8 (10/29) Review Genetic Algorithm
    9 (11/5) Midterm Exam  
    10 (11/12) ANN (SOM & BAM) Vision in Matlab
    11 (11/19) Ch 7 Logical Agents Vision in Matlab
    12 (11/26) Thanx giving Break  
    13 (12/3) Association Rule Fuzzy Theory
    14 (12/10) Ch 10 Knowledge Representation Open topic
    15 (12/17) Project presentation Project presentation

  • Evaluation:
    • Exam (30%): November 5th
    • Homework (30%): There will be 5 homework assignments. They will consist of a combination of written problems and programming asignments.
    • Project (40%): Students are required to implement one artificial intelligent system (Presentation and report required.)

  • Course Policies
    • All homeworks must be submitted at the beginning of the class. No late homework will be accepted.