GRADUATE SCHOOL

Ph.D. In Electrical-Electronics Engineering

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 -
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;
  • Discussing a broad range of issues in the field of AI
  • Using and discussing the basic techniques of the field
  • Evaluating potential applications of AI technology
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.

 



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 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

#
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. Knows and applies the research methods in studies of the area with a high level of skill.
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. Can independently realize novel studies that bring innovation to the field, or methods, or design, or known methods.
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. Performs critical analysis, synthesis and evaluation of new and complex ideas.
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 C1 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 Evaluates the results of scientific, technological and engineering research and development activities in terms of the social, environmental, health, safety and legal aspects. Examines social relations and norms related to the field, and develops and makes attempts to change them if necessary. 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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