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Faculty of Engineering Technology
Course name: Artificial Intelligence & Neural
Networks
Instructor name: Dr. Anas Quteishat
E-mail address: [email protected]
Short course description
Introduction to artificial intelligence and artificial neural networks is given. Topics include
foundations (search, knowledge representation, machine learning) and applications. Some practical
application shall be discussed as well.
Course Objectives
Introduce AI, ANNs and their applications.
Course contents
Subject
Topic 1: Introduction to AI and
Applications
Topic 2: AI as a search:
Knowledge representation.
Depth first, breadth first, hill climbing,
heuristic, best first, simulated annealing,
genetic algorithm, A, A*, iterative
deepening, Simplified memory A*, .GA.
Topic 3: Artificial neural networks, in
particular Hebb, perceptron and multilayer perceptron are studied. The back
propagation algorithm is studied
Topic 4 AI as a logic: representation
languages, propositional logic, first order
logic, inference. Introduction to Expert
system and example. Outlook to fuzzy
logic.
Decision trees
Project discussions
weeks
1
4
4
3
1
1
2
Exams
Note
More shall be given
where possible.
1
Learning Outcomes
1. Knowledge and understanding
The student should gain additional knowledge in the subject matter over and above the
knowledge accumulated from other prerequisite courses. Each student will be able to
demonstrate an understanding the theory and applications of the subject matter Intellectual
skills.
2. Subject specific skills
Faculty of Engineering Technology
Course name: Artificial Intelligence & Neural
Networks
Each student will be able demonstrate knowledge and problem-solving skills in addressing realworld situations.
Each student will be able to demonstrate effective leadership styles, teamwork and
collaborative behavior.
Each student will be able to describe the use of information technology and the role of
information resources in enhancing performance and research in this area.
Each student will be able to effectively communicate orally and in writing what he has learned
in this area.
3. Transferable skills:
The student will gain new skills in the area of the course. The skills can be in equipment handling,
use of tools, working with materials, design, etc.
Teaching methods (check the applicable methods and explain)
 lecture
Lectures are given through a data show, and are given to students in advance. And they
are pointed to the reference chapter or source.
 Demonstrations
Demonstrations of some AI and artificial Neural Networks Algorithms shall be done to
strengthen the understanding of the Area.
 Tutorial
Some tutorials in the AI field will be given where needed.
 Case Study
A full case study shall be presented.
 Assignments, reports, and projects
Each student (or a group) shall do a project in AI or artificial Neural Networks.
Grading policy
Exams
First exam
Second exam
Final exam
Projects and assignments
20%
20%
50%
10%
Text Book
Stuart Russell and Peter Norvig: Artificial Intelligence: A Modern Approach, Prentice
Hall, Second Edition.
Haykin, Neural Networks a comprehensive foundations, Prentice Hall, second edition.
References
Ivan Bratko, Prolog Programming for Artificial Intelligence Recommended: various
papers and books
Nilsson. Principle of artificial intelligence. Notes and tutorials.