ARTIFICIAL INTELLIGENCE

KYZ102

Course Code: KYZ102
Semester: Second Term (Spring)
Theory+Practice: 2 + 0
ECTS: 2

Course Type

Compulsory Course

Course Level

Associate Degree

Year of Study

Semester

Second Term (Spring)

ECTS

Name of Lecturer(s)

Instructor Fatma Yiğit Kaynak

Learning Outcomes

At the end of this course, the students;

Knows and explains the basic concepts of artificial intelligence.
Knows and explains artificial intelligence techniques, other related disciplines and areas of use.
Has knowledge of the necessary programming languages, software, tools and processes in the field of artificial intelligence.

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

None

Course Description

Targeting those who are new to the subject of artificial intelligence, it aims to provide a comprehensive overview of the field of artificial intelligence and teach participants the basic concepts and areas of use of artificial intelligence. The definition of the concept of artificial intelligence includes basic information on machine learning algorithms, artificial neural networks, language processing, and image processing, and covers application examples of the use of artificial intelligence in various fields.

Course Contents

WEEK	TOPICS
1^st Week	Introduction to Artificial Intelligence
2^nd Week	Fundamentals of Artificial Intelligence
3^rd Week	Data Science for Artificial Intelligence
4^th Week	Artificial Intelligence Development Environments
5^th Week	Machine Learning 1: Fundamentals
6^th Week	Machine Learning 2: Algorithms
7^th Week	Artificial Neural Networks (ANN)
8^th Week	Midterm Exam
9^th Week	Deep Learning Fundamentals
10^th Week	Image Processing and Computer Vision
11^th Week	Natural Language Processing (NLP)
12^th Week	Generative Artificial Intelligence
13^th Week	AI Applications in Robotic Systems
14^th Week	Artificial Intelligence Ethics and Security

Recommended or Required Reading

[1] https://www.kaggle.com/ [Kaggle]
[2] https://iapp.org/resources/article/global-ai-legislation-tracker/ [Global AI Law and Policy Tracker]
[3] https://artificialintelligenceact.com/ [The EU Artificial Intelligence Act]
[4] https://www.deeplearning.ai/ [Andrew Ng Trainings]
[5] https://openai.com/blog/chatgpt [ChatGPT]
***If you feel the need for academic accommodations due to your disability, please contact the instructor of the course.

Planned Learning Activities and Teaching Methods

Lecture
Questions/Answers
Problem Solving
Practice

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	1	40
Total(%)		40
Contribution of In-term Studies to Overall Grade(%)		40
Contribution of Final Examination to Overall Grade(%)		60
Total(%)		100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam	1	1	1
Preparation for Quiz
Individual or group work	14	1	14
Preparation for Final exam	1	14	14
Course hours	14	2	28
Preparation for Midterm exam	1	7	7
Laboratory (including preparation)
Final exam	1	1	1
Homework
Total Work Load			65
Total Work Load / 30 (h)			2.16
ECTS Credit of the Course			2

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3
K1	X	X	X
K2
K3	X	X	X
K4	X	X	X
K5	X	X	X
K6
K7
K8	X	X	X
K9
K10
K11	X	X	X
K12
K13