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Artficial Intelligence

Instructor: Dana H. Ballard

Office Hours: Wednesday and Friday 3-4pm or by appointment

Office: CSB 721

Extension: 5-3772

e-mail: dana@cs.rochester.edu

TA: Jessica Bayliss

Office hours:TBA

e-mail:bayliss@cs.rochester.edu

Overview

The goal of Artificial Intelligence is a theory of intelligent behaviors. These behaviors span the range from basic senory-motor skills to advanced problem solving.

Credit

The grade for the course will be based on weekly assignments(80%) and a final(20%).

Course Outline

You should read the text assignment or handout before coming to class.
Week Date Topic Subtopic Reading
120 Jan Artificial Intelligence 1: What is intelligence?
2: Representation and Search
Ch 1
2 27 Jan LISP exercises3: Basic LISP programming
4: Evaluation Issues
Ch 2
3 3 Feb Search5: Introduction
6: Heuristic search
Ch 4(4.1-4.4)
4 10 Feb Games, Minimax, alpha/beta 7: Minimax, Alpha-beta
8: Making symbols
Ch 4(4.5)
5 17 Feb Specialized Intelligence9: Expectation Maximization
10: Maximum Likelihood
Ch8 (8.1-8.2)
6 24 Feb Bayes Nets11: Intro to Bayes Nets
12: Bayes nets algorithms
Ch 8 (8.3)
7 3 Mar Reinforcement Learning13: Markov processes
14: Q-learning
Ch 5 (5.9)
8 17 Mar Pattern recognition 15: Neural nets, Intro
16: Case study - Backgammon
Handout
9 24 Mar The Senses 17: Basic Ontology
18: Introduction to Vision
Ch 9 (9.1-9.3)
10 31 Mar Vision 19: Correlation
20: Algorithms(LAB)
Handout
11 7 Apr Natural Language21: Natural Language Parsing
22: Nat Language (LAB)
Ch 10
12 14 Apr General Intelligence 23: Logic
24: Representation and binding
Ch 3
13 21 Apr Theorem Proving 25: Resolution
26: Intro to planning
Ch 7
1428 Apr Planning27: Planning issues
28: Summary