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
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 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.

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.

3

To be able to design and apply theoretical, experimental and model-based research; to be able to solve complex problems in such processes.

4

Being able to utilize Natural Sciences and Bioengineering principles to design systems, devices and processes.

5

To be able to follow and apply new developments and technologies in the field of Bioengineering.

6

To be able to work effectively in multi-disciplinary teams within the discipline of Bioengineering; to be able to exhibit individual work.

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.

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.

9

To be able to prepare an original thesis/term project in accordance with the criteria related to the field of Bioengineering.

10

To be able to follow information about Bioengineering in a foreign language and to be able to participate in discussions in academic environments.

11

To be able to improve the acquired knowledge, skills and qualifications for social and universal purposes regarding the studied area.

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

 


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