CS 486 Introduction to Artificial Intelligence

kettlecatelbowcornerAI and Robotics

Nov 7, 2013 (4 years and 6 days ago)

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Proposed revision to CS 486 Handbook description


CS 486 Introduction to Artificial Intelligence


Objectives

To give an introduction to the fundamental problems of artificial intelligence and an
introduction to the basic models and algorithms used in tack
ling these problems. Another
objective is to expose the student to frontier areas of computer science, while providing
sufficient foundations to enable further study.

Intended Audience

CS 486 is a course for CS major students, and is normally completed in

a student's fourth
year. The course should be relevant for all students who are interested in applications of
a computer to solve sophisticated problems.

Related Courses

Prerequisites:
CS 341
/CM 339

or SE 240; Computer Science students only.

Corequisite
: STAT 206 or 231/241.

Antirequisite: ECE 457.

References

Artificial Intelligence: A Modern Approach., 2nd Edition,
by S. Russell and P. Norvig,
Prentice
-
Hall, 2003.

Schedule

3 hours of lectures per week. Normally available in Fall, Winter and Spring.

O
utline

Introduction
(1 hr)

Introduction to artificial intelligence.
What is AI?
Goals and methodology.
Building
intelligent agents.


Problem
-
solving

(
9

hrs)

Build
ing systems that solve problems by searching.

C
o
nstraint satisfaction problems,
backtrack and
local search algorithms. Automated problem solving, graph search
algorithms, searching implicit graphs
, A* search. Automated planning.

Knowledge representation and reasoning

(
4

hr
s
)

Building systems that know facts

and reason on them to solve problems
. Kno
wledge
representation,
propositional logic
,
first order logic,
commonsense knowledge.
Logical
inference
.

Representing change. Building a knowledge base.



Uncertain knowledge and reasoning

(
10

hrs)

Building systems that reason and act in uncertain environm
ents.
Probabilistic reasoning,
joint probabilities, conditional probabilities, conditional independence, Bayes
rule,
Bayesian networks
.
Utilities
, decision theory, sequential decision making,
value of
information
. Game
theory,
multi
-
agent systems,
adversar
ial env
ironments, p
arti
ally
observable environments
.

Machine
Learning
(
10

hrs)

Building systems that improve with experience. Learning a function from examples, linear
functions, generalized linear functions (nonlinear bases), neural networks, decision tre
es.
Generalization theory, over
-
fitting and under
-
fitting, complexity control. Topics may also
include reinforcement learning and unsupervised learning.

Communicating
(
2

hrs)

Building systems that communicate.

Natural language understanding, parsing, gram
mars,
semantic interpretation, pragmatics.