CIS 467 Introduction to Artificial Intelligence

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Fall 2008



CIS 467 Introduction to Artificial Intelligence



Syllabus

Course Information
Meeting:
TuThurs
9:30AM-10:50AM
at CST 3-216 (Lecture)
M
10:35AM-11:30AM
at CST 1-214 (Labs)
Instructor Information:
Name: Andrew C. Lee, Ph. D.
Office:
RM 4-291, Center of Science and Technology, SU
Office Hours: TBA
Phone: 315-443-3029
Email:
alee18@syr.edu
Note:
This class is also listed as CIS 667/CSE 684. Put the course number you enrolled in (e.g.

CIS 467) in the subject line when sending an email to the instructor
Class web page: TBA
Course Content Management System: We may use the SU's blackboard system. The details will be

announced in class.
Course Description (taken from Course Catalog)

CIS 467/CIS 667/CSE 684 Introduction to Artificial Intelligence 3 SI Knowledge representation,

production systems, search algorithms, game playing, uncertainty handling, learning, automated

reasoning, computer vision, and natural language processing. Additional Programming project or term

paper required for
CIS 667
, not for
CIS 467
. Prereq: Knowledge of high-level programming language.
Detailed Prerequisites
:

Working knowledge in a functional programming language (LISP or Scheme)

Data structures

Algorithm complexity (i.e., Big-O notation, NP-completeness)

Basic probability theory

Basic Logic

Basic Unix commands
Required Background
In order to succeed in this course, students are expected to have the ability to:

restate, discuss, and describe fundamental concepts in discrete mathematics and data structures

(CIS 351, CSE 382) and apply and use the knowledge from these subjects.

restate, discuss, and describe fundamental concepts in logic and probability.

construct, compose, and design computer programs in a functional and object-oriented

programming design principle (Scheme, Haskel, LISP)

use basic Unix commands, editors, utilities.

measure, assess, and evaluate experimental data.
Andrew C. Lee
1
EECS-SU
Fall 2008



CIS 467 Introduction to Artificial Intelligence



Syllabus

All of these skills can be acquired by successfully completing CIS 252,

PHI 251, CIS 275, CIS 321,

CIS 351 and having the senior standing in the CIS and CSE program.
Students who do not have the

required background should contact the instructor ASAP
.
Purpose of Course
CIS 467 is an upper-division elective course for undergraduate computer science majors. CIS 667 and

CSE 684 are graduate elective course for computer science and computer engineering majors.

Undergraduate juniors and seniors take CIS 467. This course meet several of the general educational

objectives stated in the college catalog, namely:
"depth and breadth of knowledge in computer and information science as evidenced by an

understanding of computing systems and science coupled with the capacity to produce feasible and

responsible solutions to complex computing problems; literacy as evidenced by skills in writing,

reading, speaking, and listening;

critical thinking as evidenced by skills in interpretation, analysis,

evaluation, inference,

argumentation, and reflection;"

Course Objectives
Upon successful completion of this course, students are expected to have the ability to:

1.
Describe and explain the fundamental concept of artificial intelligence:

Knowledge

representation, production systems, search algorithms, game playing, planning, logic, expert

systems, neural networks, genetic algorithms, classifier systems, uncertainty handling, learning,

automated reasoning, computer vision and natural language processing.
.
2.
Define, restate, discuss, and explain philosophical aspects of artificial intelligence.
3.
Design artificial intelligence solutions to problems and construct software systems that

implement the design through LISP.
4.
Measure, evaluate, and compare different AI algorithms and their implementations through

instrumentation for performance analysis.
5.
Discuss the difference between AI solutions to traditional computer science solutions.
Major Topics (Tentative)

Chapter 1. Introduction
C
hapter 2. Intelligent Agents
Chapter 3. Solving Problems by Searching
Chapter 4. Informed Search Methods
Chapter 6. Agents that Reason Logically
Chapter 7. First-Order Logic
Chapter 9. Inference in First-Order Logic
Andrew C. Lee
2
EECS-SU
Fall 2008



CIS 467 Introduction to Artificial Intelligence



Syllabus

Chapter 10. Production Systems and Semantic Network
Chapter 11. Planning
Chapter 14. Uncertainty
Chapter 15. Probabilistic Reasoning Systems
Chapter 18. Learning from Observations
Chapter 19. Learning in Neural and Belief Networks
Special Topic: Game Theory and AI
Chapter 26. Philosophical Foundations
Chapter 27. AI: Present and Future
Textbook (Required)

Artificial Intelligence: A Modern Approach by Russell and Norvig, Prentice Hall, Englewood Cliffs,

N.J., Second Edition.
Grading
1.
Quizzes (10 %)
2.
Midterm exam (25%)
3.
F
inal (25%)
4.
Homework assignments (10%)
5.
Programming projects (30%)
Course Policies
1.
Attendance:
You are strongly encouraged to attend the lectures and the labs.
2.
Grades:
1.
Questions regarding any graded work must be brought to the attention of the instructor in

writing within 7 days when the graded work is returned to class.
2.
It is your responsibility to keep track of all your grades and to make sure that they are

recorded correctly. The instructor will have printouts available after each exam.
3.
The weakest grade of your homework assignment and quizzes may be dropped.
3.
Late Policies:
For each assignment and project, deadlines and late policies are stated therein.

Student are responsible to contact the instructor ASAP if there are University Acceptable

reasons (e.g. Illness etc.).
4.
Academic Rules and Regulations:
Students are required to read the following document:
http://www.syr.edu/publications/undergradcat/rulesandregs.pdf
5.
Email: Use the official University email address to communicate with the instructor. You are

Andrew C. Lee
3
EECS-SU
Fall 2008



CIS 467 Introduction to Artificial Intelligence



Syllabus

required to check your SU email at least once a day.
Academic Integrity
:
The University is committed to
support academic honesty and creating a climate

of academic integrity in the classroom. You are strongly advised to read the following:
http://academicintegrity.syr.edu/index.php?page=students
In this course, labs are typically done separately but interaction is encouraged. However, the
homeworks and final project are to be your own work.
Cheating is taken seriously in this course.

Possible consequences:

a 0 on the assignment,

a 0 for the course,

referral to a university judicial board,

or some other appropriate response.
The Dean's office will be informed of any incidents of academic dishonesty.
DO NOT CHEAT
. If you get a 0 on one assignment because you don't do it, it won't hurt that bad. Not

learning the covered material is a problem, but that will be just as much of a problem if you copy

someone else's code. Handing in a mediocre assignment that you did is way better than handing in a

great assignment that someone else did.
Andrew C. Lee
4
EECS-SU