The University of Jordan-Aqaba
Faculty of Computer Information Technolgy
Computer Information Systems Department
First Semester 2014/2015
Course: Artificial Intelligence AI (5401342)
Semester and Year: fall/ 2014/2015
Pre-requisites: Data Structures
Instructor: Evon Abu-Taieh, PhD
Office : 207
Office hours : Sun, Mon, Tues 8-10, Tues & Thurs 8-11
Email: abutaieh@gmail.com, e.abutaieh@ju.edu.jo
Intended Learning Outcomes:
Successful completion of this module should lead to the following learning outcomes:
A-Knowledge and Understanding (students should)
(A1) have some understanding of the basic concepts and techniques of AI
(A2) have some understanding of Prolog
(A3) have some understanding of the basic concepts of ANN, GA, Fuzzy Logic.
(A4) have some understanding of some blind and heuristic search techniques.
B-Intellectual skills-with ability to
(B1) Appreciate the subtleties related to different approaches to AI
(B2) Appreciate the subtleties related to different AI techniques.
(B3) Decide the suitability of AI techniques for a problem/domain.
C- Practical Skills-With ability to
(C1) Implement a KBS for a simple domain.
(C2) Write simple AI programs in PROLOG.
(C3) Express knowledge of a domain in a suitable knowledge representation formalism.
D-Transferable Skills-With ability to
(D1) Deploy communication skills.
(D2) Work effectively within a group to analyse, design and implement a AI system.
(D3) To work to tight deadlines
(D4) effectively present the final work in a demo.
This course is appropriate for undergraduate curricula at a third/four-year IT school.
Teaching Methods
Tutorials
Method
Lectures
Demos
Objective
Al, A2, A3
A2
A2, A3
Learning
B1, B2, B3,
B1, B5, B6
B1, B2, B3,
Outcomes
C1, C2, C3
C2, C6
C1, C2, C3
D2
D1, D2, D4
D1, D2, D3, D4
Exams + H.W
Exams + Presentation
Assessments
Project + Presentation
Laboratory
Project + H.W
Case Study
Exams + Project + Presentation
Outline
1. Introduction to AI
 Concepts and Definitions
 Perception(Computer vision, Speech recognition, Natural Language processing)
 Reasoning (Knowledge representation, Search Optimization, decision/game
theory, machine learning)
 Fields of AI
2. Neural Computing: The Basics
W3,W4
3. Back propagation
4. Genetic Algorithms –Knapsack
W4,W5
5. NP Hard Problems
W56,7
a. Travelling Salesman Problem (TSP)
b. Knapsack
c. Stable marriage problem
d. Shortest Path
Mid Term Exam
6.
7.
8.
9.
10.
11.
Validity
Programming in Prolog
Artificial Intelligence Programming in Prolog
Methods of Inference
Fuzzy Logic
Presentation of topics (hopfield, ART, ANN)
W8
w9,10
w11,12
w13
w14
References:

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
 Artificial Intelligence: A Modern Approach, 2/E Stuart Russell, University of
California, Berkeley, Peter Norvig, Google Inc.
 http://mathsite.math.berkeley.edu/smp/smp.html
 http://www.coli.uni-sb.de/~kris/learn-prolog-now/)
 http://www.cs.brown.edu/courses/cs141/lectures.html
 http://www.markwatson.com/opencontent/opencontent.htm
Homework:
Knapsack, Random numbers, ANN-Bp, presentation
Grades
• Homework & Projects: 20%
• Genetic algorithms
• ANN
• Prolog Family
• Hopfield
• Matching algorithm : Psudo Code.
• Exam #1:15%
• Exam #2:15%
•
Final: 50%
Intended Grading Scale:
95
90
85
80
75
70
65
60
55
50
0
100
94
89
84
79
74
69
64
59
54
49
A
AB+
B
BC+
C
CD
DF