CIS 630

CIS 630 Survey of Artificial Intelligence I: Basic Techniques

Autumn, 2001, 3 credits
MWF 9:30-10:18 am, Dreese Lab. 317

web site: www.cis.ohio-state.edu/~szhu/cis630

Description

A survey of the basic concepts and techniques of Artificial Intelligence including problem formulation, problem solving, knowledge representation, reasoning, and planning. Three AI perspectives shall be discussed: logic, statistical, and biologically motivated methods.

Objectives

Upon satisfactory completion of the course, you will have learned:

Prerequisites

Course Requirements

Textbooks

Homework and Exams:

Assignments are due at the beginning of class
You are allowed for up to 2 late days for all homeworks and labs in the whole quarter. You may use the late days for personal or family emergency (weekend will not be counted as late days). No late homework/lab will be accepted after the late days are used.

Teaching Staff

  • Song-Chun Zhu, Instructor
    szhu@cis.ohio-state.edu
    Office Hour: Monday 4-5:00pm, or by appointment.
    Office Location: room 585, Dreese Lab.
    Office Phone: 292-4890
  • Teaching Assistant: Rohan Kurian
    Office Hour: M.W, 10:15-11:15pm.
    Office Location: Caldwell lab 408
    Office Phone:
    email: kurian@cis.ohio-state.edu

    General Topics

    Number of Weeks Topics
    3 Basic AI concepts, representations and search algorithms
    2.5 Knowledge representation and reasoning using logic methods
    --- propositional and predicate calculus
    2 Knowledge representation and reasoning using statistical methods
    --- uncertainty reasoning, Bayes nets
    1.5 Knowledge representation and Reasoning using biologic methods
    --- genetic algorithm, neural nets
    1 Other topics

    Tentative Schedule of lectures

    Day Date Topic Readings Assignment
    Wed 09/19 What is artificial intelligence? Ch. 1 Lect1.ppt HW1.pdf out
    Fri 09/21 Intelligent agents Ch. 2 Lect2.ppt None
    Mon 09/24 Problem formulation, state space Ch. 3.1-3.4 Lect3.ppt None
    Wed 09/26 Uninformed search algorithms Ch. 3.5-3.8 Lect4.ppt Hw1 in, HW2.pdf out
    Fri 09/28 Informed search algorithms Ch. 4 Lect5.ppt None
    Mon 10/01 A* algorithm and its analysis Ch. 4 None
    Wed 10/03 Adversarial search, game playing Ch. 5 Hw2 in, HW3.pdf out
    Fri 10/05 Alpha-beta pruning Ch. 5 Lect6.ppt None
    Mon 10/08 Alpha-beta analysis, Review None None
    Wed 10/10 Optimization search: genetic algorithm Ch. 20.8 + Handout Hw 3 in, HW4.pdf, lab1 and lab2 out
    Fri 10/12 Optimization search: Intro. to MCMC Lect7.pdf None
    Mon 10/15 AI Video:Real life and artificial life None None
    Wed 10/17 More on Search Algorithms None Hw4 in
    Fri 10/19 Knowledge Representation Lect8.ppt None
    Mon 10/22 Propositional calculus Ch. 6.4-6.5 Lect9.ppt None
    Mon 10/24 Predicate calculus Ch. 7.1-7.6 None
    Fri 10/26 Predicate calculus, Midterm review Ch. 6-8, midterm_review Lab 1 and 2 in
    Mon 10/29 Midterm None None
    Wed 10/31 Inference with predicate calculus Ch. 9.1-9.3 Lect10.ppt Hw5 out
    Fri 11/2 Inference with predicate calculus Ch. 9.4-9.7 None
    Mon 11/5 Uncertainty, calculus of prob. Ch. 14 None
    Wed 11/7 Probabilistic Reasoning Ch. 15.1-15.2 Lect11.ppt HW5.pdf in
    Fri 11/9 Probabilistic Reasoning Ch. 15.3 Hw6 out
    Mon 11/12 Holiday, no class None None
    Wed 11/14 Probabilistic Reasoning Ch. 15 None
    Fri 11/16 Intro. to Neural net Ch. 19.1-19.4 Hw7 out
    Mon 11/19 Learning in neural net Ch. 19.4-19.5 None
    Wed 11/21 Learning decision tree Ch. 18.3, handout None
    Fri 11/23 Holiday, No Class None None
    Mon 11/26 Learning decision tree handout Hw7 in
    Wed 11/28 Visual perception Ch. 24 None
    Fri 11/30 AI: present and future Ch. 26-27 None