GRADUATE SCHOOL
M.SC. in Computer 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
|
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| Course Language |
English
|
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| Course Type |
Elective
|
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| Course Level |
Second Cycle
|
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | Problem SolvingQ&ACritical feedbackLecture / 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;
|
| Course Description |
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| 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 | Accesses information in breadth and depth by conducting scientific research in Computer Engineering; evaluates, interprets and applies information. |
X | ||||
| 2 | Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations. | X | ||||
| 3 | Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines. |
X | ||||
| 4 | Is informed about new and upcoming applications in the field and learns them whenever necessary. | X | ||||
| 5 | Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions. |
X | ||||
| 6 | Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs | |||||
| 7 | Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process. |
X | ||||
| 8 | Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility. |
|||||
| 9 | Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale. |
X | ||||
| 10 | Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. |
|||||
| 11 | Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications. |
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| 12 | Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest