Information Package / Course Catalogue - Adnan Menderes University

General Information Unit Staff Academic Information Programme Outcomes Course Structure Diagram Contact Information

Person Responsible for Information Package

Introduction to Artificial Intelligence and İts Fundamentals

Course Code	: YZO103
Course Type	: Required
Couse Group	: Short Cycle (Associate's Degree)
Education Language	: Turkish
Work Placement	: N/A

Theory	: 2
Prt.	: 0
Credit	: 2
Lab	: 0
ECTS	: 4

Objectives of the Course

It aims to raise students' awareness of the fundamental concepts, history, application areas, and ethical responsibilities of artificial intelligence. It also aims to develop analytical thinking and problem-solving skills by teaching them introductory algorithms, models, and technical skills.

Course Content

Basic concepts, history and application areas of artificial intelligence.

Name of Lecturer(s)

Ins. Ümit BULUT

Learning Outcomes

1.	Knows basic artificial intelligence concepts.
2.	Learn the commonly used artificial intelligence techniques and their importance.
3.	Gains knowledge of the programming languages, software, tools and processes required to develop artificial intelligence.
4.	Learn the sub-branches of artificial intelligence, its products and real-life usage areas.
5.	Knows the characteristics of basic types of artificial intelligence.

Recommended or Required Reading

1.	Mehmet Özkan, Introduction to Machine Learning and Applications, Kodlab.
2.	Güven, F. (2021). Introduction to Data Science and Applications with Python. Ankara: Academic Publications.

Weekly Detailed Course Contents

Week 1 - Theoretical

History and Philosophy of Artificial Intelligence Historical Development and Current Situation in Turkey

Week 2 - Theoretical

The Structure of the Human Mind and Basic Concepts of Artificial Intelligence

Week 3 - Theoretical

This way, we will reveal what the basic types of artificial intelligence, especially machine learning, deep learning, neural networks and algorithms are and how they differ from each other.

Week 4 - Theoretical

Artificial Intelligence Problem Solving with Heuristic Algorithms and Search and Introduction to Search Algorithms

Week 5 - Theoretical

Search Algorithms That Do Not Use Problem Information

Week 6 - Theoretical

Heuristic algorithms and Search

Week 7 - Theoretical

Game Problems

Week 8 - Theoretical

Meta-Heuristic Search Methods (Midterm Exam)

Week 9 - Theoretical

Artificial Neural Networks

Week 10 - Theoretical

Knowledge-Based Agents

Week 11 - Theoretical

Machine Learning: induction, instruction learning, learning by example

Week 12 - Theoretical

Supervised Learning Algorithms

Week 13 - Theoretical

Unsupervised Learning Algorithms

Week 14 - Theoretical

Genetic Algorithm

Assessment Methods and Criteria

Type of Assessment	Count	Percent
Midterm Examination	1	%40
Final Examination	1	%60

Workload Calculation

Activities	Count	Preparation	Time	Total Work Load (hours)
Lecture - Theory	14	1	2	42
Project	3	8	4	36
Midterm Examination	1	10	1	11
Final Examination	1	10	1	11
TOTAL WORKLOAD (hours)				100

Contribution of Learning Outcomes to Programme Outcomes

	PÇ-1	PÇ-2	PÇ-3	PÇ-4	PÇ-5	PÇ-6	PÇ-7	PÇ-8	PÇ-9	PÇ-10	PÇ-11	PÇ-12
OÇ-1	4	4	4	5	4	5	4	5	4	5	5	5
OÇ-2	4	5	4	5	4	5	4	5	4	5	4	5
OÇ-3	3	4	4	4	5	4	4	4	5	4	4	5
OÇ-4	5	5	4	5	4	4	4	4	5	5	5	5
OÇ-5	5	5	5	5	4	4	4	5	4	4	5	4

Person Responsible for Information Package