GRADUATE SCHOOL
M.SC. in Bioengineering (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 | ||
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 be able to have adequate knowledge in Mathematics, Life Sciences and Bioengineering; to be able to use theoretical and applied information in these areas to model and solve Bioengineering problems. |
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2 | To be able to use scientific methods to complete and apply information from uncertain, limited or incomplete data; to be able to combine and use information from related disciplines. |
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3 | To be able to design and apply theoretical, experimental and model-based research; to be able to solve complex problems in such processes. |
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4 | Being able to utilize Natural Sciences and Bioengineering principles to design systems, devices and processes. |
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5 | To be able to follow and apply new developments and technologies in the field of Bioengineering. |
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6 | To be able to work effectively in multi-disciplinary teams within the discipline of Bioengineering; to be able to exhibit individual work. |
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7 | To be able to have the knowledge about the social, environmental, health, security and law implications of Bioengineering applications, to be able to have the knowledge to manage projects and business applications, and to be able to be aware of their limitations in professional life. |
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8 | To be able to have the social, scientific and ethical values in the stages of collection, interpretation, dissemination and application of data related to the field of Bioengineering. |
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9 | To be able to prepare an original thesis/term project in accordance with the criteria related to the field of Bioengineering. |
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10 | To be able to follow information about Bioengineering in a foreign language and to be able to participate in discussions in academic environments. |
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11 | To be able to improve the acquired knowledge, skills and qualifications for social and universal purposes regarding the studied area. |
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12 | To be able to recognize regional and global issues/problems, and to be able to develop solutions based on research and scientific evidence related to Bioengineering. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest