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M.SC. in Electrical and Electronics Engineering (With Thesis)
IES 503 | Course Introduction and Application Information
| Course Name |
Artificial Intelligence
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
IES 503
|
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 | - | |||||
| 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 | Common LISP and Prolog; Intelligent Agents; Problemsolving and Search: uninformed and heuristic search, A*, local search and optimization; Constraint satisfaction problems; Game playing and adversarial search; Logical reasoning. Propositional Logic. Firstorder logic. Inference in firstorder logic; Planning; Reasoning under uncertainty. Bayes rule. Belief networks. Using beliefs to make decisions. Learning beliefs; Special topics: Robotics, Natural Language Processing, Game Theory, other AI applications. |
|
|
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 to AI. Brief history | Artificial Intelligence: A Modern Approach (second edition) by Stuart Russell and Peter Norvig. Chapter 1. | |
| 2 | Introduction to AI & Applications of AI | Artificial Intelligence: A Modern Approach (second edition) by Stuart Russell and Peter Norvig. Chapter 1. | |
| 3 | Introduction to Lisp & Prolog | Lecture notes | |
| 4 | Intelligent Agents | Artificial Intelligence: A Modern Approach (second edition) by Stuart Russell and Peter Norvig.Chapter 2. | |
| 5 | Problem Solving by Search (Informed Search, Uninformed Search, Adversarial Search) | Artificial Intelligence: A Modern Approach (second edition) by Stuart Russell and Peter Norvig. Chapter 3-4-5 | |
| 6 | Constraint Satisfaction Problems & Special Topics: Machine Learning | Artificial Intelligence: A Modern Approach (second edition) by Stuart Russell and Peter Norvig. Chapter 6 | |
| 7 | First Order Logic / Inference in First Order Logic & Special Topic: Robotics | Artificial Intelligence: A Modern Approach (second edition) by Stuart Russell and Peter Norvig. Chapter 8 &9 | |
| 8 | Logical Agents & Special Topics: Natural Language Processing | Artificial Intelligence: A Modern Approach (second edition) by Stuart Russell and Peter Norvig. Chapter 7. | |
| 9 | Uncertainty and Probabilistic Reasoning & Special Topics: Expert Systems | Artificial Intelligence: A Modern Approach (second edition) by Stuart Russell and Peter Norvig. Chapter 13 &14 | |
| 10 | Special Topics:Genetic Algorithms & Information Retrieval | Lecture Notes | |
| 11 | Special Topics: Planning & Computer Vision | Lecture Notes | |
| 12 | Special Topics: Artificial Neural Networks | Lecture Notes | |
| 13 | Project Presentations I | - | |
| 14 | Project Presentations II | ||
| 15 | Discussions- Review | ||
| 16 | - |
| Course Notes/Textbooks | The textbook referenced above and course slides |
| Suggested Readings/Materials | Related Research Papers |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
1
|
25
|
| Presentation / Jury |
1
|
25
|
| Project |
1
|
50
|
| Seminar / Workshop |
-
|
-
|
| Oral Exams | ||
| Midterm | ||
| Final Exam | ||
| Total |
| Weighting of Semester Activities on the Final Grade |
100
|
|
| Weighting of End-of-Semester Activities on the Final Grade | ||
| 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 |
1
|
30
|
30
|
| Presentation / Jury |
1
|
35
|
35
|
| Project |
1
|
52
|
52
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
0
|
||
| Final Exam |
-
|
0
|
|
| Total |
225
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
PC Sub | Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
|||
| 1 | Accesses information in breadth and depth by conducting scientific research in Electrical and Electronics Engineering; evaluates, interprets and applies information |
-
|
-
|
-
|
-
|
-
|
|
| 2 | Is well-informed about contemporary techniques and methods used in Electrical and Electronics Engineering and their limitations |
-
|
-
|
-
|
-
|
-
|
|
| 3 | Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines |
-
|
-
|
-
|
-
|
-
|
|
| 4 |
Is informed about new and upcoming applications in the field and learns them whenever necessary. |
-
|
-
|
-
|
-
|
-
|
|
| 5 | Defines and formulates problems related to Electrical and Electronics Engineering, develops methods to solve them and uses progressive methods in solutions. |
-
|
-
|
-
|
-
|
-
|
|
| 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 modeling; analyses and resolves the complex problems that arise in this process. |
-
|
-
|
-
|
-
|
-
|
|
| 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. |
-
|
-
|
-
|
-
|
-
|
|
| 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 Electrical and Electronics Engineering applications, knows their project management and business applications, and is aware of their limitations in Electrical and Electronics Engineering applications. |
-
|
-
|
-
|
-
|
-
|
|
| 12 |
Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. Adheres to the principles of research and publication ethics. |
-
|
-
|
-
|
-
|
-
|
|
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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