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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 SolvingQ&ACritical feedbackLecture / Presentation | |||||
| National Occupation Classification | - | |||||
| 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;
|
| Course Description |
|
|
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 | Learning Outcome |
| 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
|
#
|
PC Sub | 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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