GRADUATE SCHOOL

M.SC. In Industrial Engineering (With Thesis)

CE 533 | Course Introduction and Application Information

Course Name
Artificial Intelligence
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 533
Fall/Spring
3
0
3
7.5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
Second Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Q&A
Critical feedback
Lecture / Presentation
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives Artificial Intelligence (AI) is devoted to the computational study of intelligent behavior. The element that the fields of AI have in common is the creation of agents/machines that can "think". This course will cover a broad technical introduction to the techniques that enable agents/computers to behave intelligently: problem solving, representing knowledge, reasoning, learning, perceiving, and interpreting. The bulk of this course reflects this diversity. We will examine the fundamental questions and issues of AI and will explore the essential techniques. In the special topics, several AI applications will be presented.
Learning Outcomes The students who succeeded in this course;
  • will be able to discuss a broad range of issues in the field of AI.
  • will be able to use and discuss the basic techniques of the field.
  • will be able to evaluate potential applications of AI technology.
  • will be able to identify problems that are amenable to solution by AI methods, and which AI methods may be suited to solving a given problem.
  • will be able to implement basic AI algorithms (e.g., standard search algorithms).
Course Description

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction Chapter 1
2 Intelligent Agents Chapter 2
3 Solving Problems by Searching Chapter 3
4 Local Search Chapter 4
5 Adversarial Search Chapter 5
6 Constraint Satisfaction Problems Chapter 6
7 Logical Agents, Propositional Logic Chapter 7
8 First-Order Logic Chapter 8
9 Inference in FOL Chapter 9
10 Classical Planning Chapter 10
11 Uncertainty Chapter 13 & 14
12 Learning Chapter 18
13 Reinforcement Learning Chapter 21
14 Reinforcement Learning Chapter 21
15 Paper Presentations
16 Final Review

 

Course Notes/Textbooks

S.Russell, P.Norvig, Artificial Intelligence:  A Modern Approach, 3rd Edition, Prentice Hall, 2010

Suggested Readings/Materials

Nick Bostrom, Superintelligence: Paths, Dangers, Strategies

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
3
15
Presentation / Jury
1
25
Project
Seminar / Workshop
Oral Exams
Midterm
1
20
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
5
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
15
4
60
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
3
10
30
Presentation / Jury
1
12
12
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
35
35
Final Exam
1
40
40
    Total
225

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have an appropriate knowledge of methodological and practical elements of the basic sciences and to be able to apply this knowledge in order to describe engineering-related problems in the context of industrial systems.

2

To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment.

3

To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality.

4

To be able to conduct basic research and write and publish articles in related conferences and journals.

5

To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results.

6

To be able to manage decision-making processes in industrial systems.

7

To have an aptitude for life-long learning; to be aware of new and upcoming applications in the field and to be able to learn them whenever necessary.

8

To have the scientific and ethical values within the society in the collection, interpretation, dissemination, containment and use of the necessary technologies related to Industrial Engineering.

9

To be able to design and implement studies based on theory, experiments and modeling; to be able to analyze and resolve the complex problems that arise in this process; to be able to prepare an original thesis that comply with Industrial Engineering criteria.

10

To be able to follow information about Industrial Engineering in a foreign language; to be able to present the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.

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

 


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