PhD in Mechatronic Engineering (English) with a bachelor's degree
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

General course introduction information

Course Code:

AIE503

Course Name:

Introduction to AI Engineering

Course Semester:

Fall

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	10

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Department Elective

Course Level:

PhD

TR-NQF-HE:8. Master`s Degree

QF-EHEA:Third Cycle

EQF-LLL:8. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Prof. Dr. BEKİR TEVFİK AKGÜN

Course Lecturer(s):

Dr.Öğr.Üyesi SİNA ALP

Course Assistants:

Course Objective and Content

Course Objectives:	Introduction to Artificial Intelligence. Heuristic problem solving. State spaces. Serching at state spaces. Games. Minimum spanning tree. Knowledge modeling. Representing knowledge. Logic. Neural networks. Fuzzy Logic.
Course Content:	Introduction to Artificial Intelligence. Heuristic problem solving. State spaces. Serching at state spaces. Games. Minimum spanning tree. Knowledge modeling. Representing knowledge. Logic. Neural networks. Fuzzy Logic.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Knows fundamental concepts about artificial intelligence
2) Knows fundamental concepts about machine learning.
3) Knows search algorithms.
4) Knows fundamental concepts about game theory.
5) Knows fundamental comcepts about logic and reasoning.
2 - Skills
Cognitive - Practical
1) Able to use artificial neural networks for the modelling of real-world problems.
2) Able to model knowledge-based systems.
3) Able to use Prolog for logic programming in elementary level.
4) Able to design natural language processing systems using hidden markov model.
5) Able to design convolutional neural network system for computer vision.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	Fundamental concepts of artificial intelligence
2)	Machine learning
3)	Artificial neural networks
4)	Game theory
5)	Problem solving: Uninformed search agents
6)	Problem solving: Informed search agents
7)	Knowledge-based agents: The Wumpus World
8)	Midterm
9)	Logic and reasoning
10)	Logic Programming: Prolog
11)	Fundamental concepts about voice and vision recognition
12)	Voice recognition using hidden markov models
13)	Image recognition based on convolutional neural network.
14)	Project presentations

Sources

Course Notes / Textbooks:	Artificial Intelligence: A Modern Approach. Stuart Russell, Peter Norvig, Prentice Hall, Second Edition Yapay Zeka, Prof.Dr.Vasif V.Nabiyev
References:	Yapay Zeka Geçmişi ve Geleceği, Nils J. Nilson Introduction to Algorithms, Cormen. Makine Öğrenmesi, Ethem Alpaydın

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	4	5	5	6	7	8	9	10
Program Outcomes
1) Knowledge and ability to apply the interdisciplinary synergetic approach of mechatronics to the solution of engineering problems
2) Ability to design mechatronic products and systems using the mechatronics approach
3) Knowledge and ability to analyze and develop existing products or processes with a mechatronics approach
4) Ability to communicate effectively and teamwork with other disciplines
5) Understanding of performing engineering in accordance with ethical principles
6) Understanding of using technology with awareness of local and global socioeconomic impacts
7) Approach to knowing and fulfilling the necessity of lifelong learning

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Knowledge and ability to apply the interdisciplinary synergetic approach of mechatronics to the solution of engineering problems
2)	Ability to design mechatronic products and systems using the mechatronics approach
3)	Knowledge and ability to analyze and develop existing products or processes with a mechatronics approach
4)	Ability to communicate effectively and teamwork with other disciplines
5)	Understanding of performing engineering in accordance with ethical principles
6)	Understanding of using technology with awareness of local and global socioeconomic impacts
7)	Approach to knowing and fulfilling the necessity of lifelong learning

Learning Activity and Teaching Methods

Expression
Individual study and homework
Lesson
Problem Solving
Project preparation
Report Writing
Q&A / Discussion
Application (Modelling, Design, Model, Simulation, Experiment etc.)

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Application
Individual Project
Presentation
Reporting
Bilgisayar Destekli Sunum

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Presentation	1	% 10
Project	1	% 30
Midterms	1	% 20
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	15	3	45
Presentations / Seminar	1	30	30
Project	1	48	48
Midterms	1	70	70
Paper Submission	1	12	12
Final	1	100	100
Total Workload			305