CLICK HERE FOR THE COURSE SYLLABUS (.pdf)

COURSE INTRODUCTION AND APPLICATION INFORMATION

 
Course Name
Code
Semester
Theory
(hour/week)
Application/Laboratory
(hour/week)
Local Credits
ECTS
Artificial Intelligence and Expert Systems
SE 420
Fall/Spring
3
0
3
4

Prerequisites
None

Course Language
English
Course Type
Elective
Course Level
First Cycle
Course Coordinator -
Course Lecturer(s) -
Course Assistants -
Course Objectives The goal of this course is to provide students with a survey of different aspects of Artificial Intelligence (AI).
Course Learning Outcomes The students who succeeded in this course;
  • Be able to develop a variety of approaches with general applicability.
  • Be able to understand AI search models and generic search strategies.
  • By using Bayesian networks , be able to use the probability as a mechanism for handling uncertainty in AI.
  • Be able to explore the design of AI systems that use learning to improve their performance on a given task.
  • Be able to present logic as a formalism for representing knowledge in AI systems.
  • Be able to address specific domains such as computer vision, natural language processing, and robotics.
Course Content This course provides an introduction to Artificial Intelligence (AI). In this course we will study a number of theories, mathematical formalisms, and algorithms, that capture some of the core elements of computational intelligence. We will cover some of the following topics: search, logical representations and reasoning, automated planning, representing and reasoning with uncertainty, decision making under uncertainty, and learning.

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction, history, Chapter 1 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 1
2 Intelligent agents, Chapter 2 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 2
3 Intelligent agents contd. Chapter 2 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 2
4 Problem solving, uninformed search, Chapter 3 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 3
5 A* search and heuristic functions, Local search, Chapter 4.14.24.34.4 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 4
6 Online search, Constraint satisfaction, Chapter 4.55.15.2 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 4
7 Constraint satisfaction contd., Gameplaying, Chapter 5.35.46.16.3 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 5
8 Gameplaying contd., Chapter 6.46.7 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 6
9 Logical agents; propositional logic, Inference in propositional logic, Chapter 7.17.47.57.7 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 7
10 Firstorder logic, Chapter 8.18.3 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 8
11 Inference in firstorder logic, Chapter 9.19.2 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 9
12 Inference contd., logic programming, Chapter 9.39.4 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 9
13 Planning problems, Chapter 11.111.2 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 11
14 Sample Expert Systems Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 12
15 Review of the Semester  
16 Review of the Semester  

 

SOURCES

Course Notes / Textbooks Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114.
References Internet

 

EVALUATION SYSTEM

Semester Requirements Number Percentage of Grade
Attendance/Participation
Laboratory
13
40
Application
Field Work
Special Course Internship (Work Placement)
Quizzes/Studio Critics
Homework Assignments
Presentation/Jury
Project
Seminar/Workshop
Midterms/Oral Exams
1
30
Final/Oral Exam
1
30
Total

PERCENTAGE OF SEMESTER WORK
70
PERCENTAGE OF FINAL WORK
1
30
Total

 

COURSE CATEGORY

Course Category

 Core Courses
Major Area Courses
Supportive Courses
X
Media and Managment Skills Courses
Transferable Skill Courses

 

THE RELATIONSHIP BETWEEN COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS

#
 Program Qualifications / Outcomes
* Level of Contribution
1
2
3
4
5
1 Be able to define problems in real life by identifying functional and nonfunctional requirements that the software is to execute X
2 Be able to design and analyze software at component, subsystem, and software architecture level X
3 Be able to develop software by coding, verifying, doing unit testing and debugging X
4 Be able to verify software by testing its behaviour, execution conditions, and expected results X
5 Be able to maintain software due to working environment changes, new user demands and the emergence of software errors that occur during operation X
6 Be able to monitor and control changes in the software, the integration of software with other software systems, and plan to release software versions systematically X
7 To have knowledge in the area of software requirements understanding, process planning, output specification, resource planning, risk management and quality planning
 X
8 Be able to identify, evaluate, measure and manage changes in software development by applying software engineering processes X
9 Be able to use various tools and methods to do the software requirements, design, development, testing and maintenance X
10 To have knowledge of basic quality metrics, software life cycle processes, software quality, quality model characteristics, and be able to use them to develop, verify and test software X
11 To have knowledge in other disciplines that have common boundaries with software engineering such as computer engineering, management, mathematics, project management, quality management, software ergonomics and systems engineering X
12 Be able to grasp software engineering culture and concept of ethics, and have the basic information of applying them in the software engineering X
13

Be able to use a foreign language to follow related field publications and communicate with colleagues

 X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

ECTS / WORKLOAD TABLE

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
16
3
Laboratory
Application
Special Course Internship (Work Placement)
Field Work
Study Hours Out of Class
16
3
Presentations / Seminar
Project
Homework Assignments
Quizzes
Midterms / Oral Exams
1
9
Final / Oral Exam
1
15
    Total Workload

CLICK HERE FOR THE COURSE SYLLABUS (.pdf)

 
 

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