Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits
8	EEE 409	INTRODUCTION TO ARTIFICIAL INTELLIGENCE	3+0+0	3	5

Course Details

Language of Instruction	English
Level of Course Unit	Bachelor's Degree
Department / Program	ELECTRICAL-ELECTRONICS E.
Mode of Delivery	Face to Face
Type of Course Unit	Elective
Objectives of the Course	This course aims to provide students the knowledge about the basic techniques and methodologies of artificial intelligence and abilities to apply artificial intelligence methods on practical problems.
Course Content	Basic concepts and terminology of AI Problem solving in AI: Basic search strategies, Supervised and unsupervised learning. Introduction to neural networks Introduction to machine learning Introduction to deep learning Applications and examples of AI
Course Methods and Techniques
Prerequisites and co-requisities	None
Course Coordinator	Associate Prof.Dr. Serkan ÖZBAY
Name of Lecturers	Asist Prof.Dr. SERKAN ÖZBAY
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	Lecture Notes
	MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence Phil Kim, APRESS. Neural Network Design, Second Edition Martin T. Hagan Howard B. Demuth Mark Hudson Beale Orlando De Jesus

Course Category

Mathematics and Basic Sciences	%20
Engineering	%40
Engineering Design	%40

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Course Duration	14	3	42
Hours for off-the-c.r.stud	14	3	42
Mid-terms	2	20	40
Final examination	1	30	30
Total Work Load			Number of ECTS Credits 5 154

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Students understand what is AI, its applications and use cases and how it is transforming our lives.
2	Students gain a historical perspective of AI and its foundations.
3	Students become familiar with basic principles of AI toward problem solving, inference, perception, knowledge representation, and learning.
4	Students explain terms like Machine Learning, Deep Learning and Neural Networks.
5	Students investigate applications of AI techniques in intelligent agents, expert systems, artificial neural networks and other machine learning models.
6	Students explore the current scope, potential, limitations, and implications of intelligent systems.

Weekly Detailed Course Contents

Week	Topics	Study Materials
1	Basic concepts and terminology of AI	Reading related lecture notes
2	Basic concepts and terminology of AI	Reading related lecture notes
3	Problem solving in AI: Basic search strategies, Supervised and unsupervised learning.	Reading related lecture notes
4	Problem solving in AI: Basic search strategies, Supervised and unsupervised learning.	Reading related lecture notes
5	Introduction to neural networks	Reading related lecture notes
6	Introduction to neural networks	Reading related lecture notes
7	Introduction to neural networks	Reading related lecture notes
8	Introduction to machine learning	Reading related lecture notes
9	Introduction to machine learning	Reading related lecture notes
10	Introduction to deep learning	Reading related lecture notes
11	Introduction to deep learning	Reading related lecture notes
12	Introduction to deep learning	Reading related lecture notes
13	Applications and examples of AI	Reading related lecture notes
14	Applications and examples of AI	Reading related lecture notes

Contribution of Learning Outcomes to Programme Outcomes

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https://obs.gantep.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=393843&lang=en