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Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
GEP0020	Artificial Intelligence for Everyone	Fall	3	0	3	5

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	GE-Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Hybrid
Course Coordinator :
Course Objectives:	Artificial Intelligence is one of the most important technologies that we use in many fields, especially our smartphone's applications, which are closest to us in daily life, and feed with our data. Artificial Intelligence will be given the requirements for non-technical people to understand artificial intelligence, not only for developers, engineers or academics to understand. Basic knowledge about how to use artificial intelligence outside technical areas to solve problems, make discoveries and change the world.

Learning Outcomes

The students who have succeeded in this course;
The students who have succeeded in this course;
1) Explain the concept of artificial intelligence and its real-world equivalence.
2) Explain the meanings behind common artificial intelligence terminology including neural networks, machine learning, deep learning and data science.
3) Use machine learning and data science projects.
4) Take part in ethical and social debates surrounding artificial intelligence.

Course Content

In this course, the students will learn the concept and history of artificial intelligence and understand the relationship between data and artificial intelligence technology. Gain knowledge about technical concepts such as machine learning, deep learning and artificial intelligence provision. Gain basic knowledge about artificial neural networks and learning algorithm. It will discuss how to use artificial intelligence effectively in real world business models and in which areas it can be integrated innovatively. The students will learn how an artificial intelligence project is realized and where to start. Have knowledge about the bias and limitations of artificial intelligence related to data and usage. Will be able to discuss the impact on business and society from an ethical and social perspective. Gain awareness about the development of artificial intelligence and the opening of new business areas.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	What is the Artificial Intelligence, history and milestones of AI, related fields
2)	What is data, relation of AI and data
3)	Artificial intelligence in real world, types of AI, classification principles
4)	What is Machine learning, types of ML, training models and usage areas
5)	The relationship between artificial intelligence, machine learning and deep learning concepts"
6)	Artificial neural networks and learning algorithm
7)	Advanced neural networks to understand data and use data in different business lines
8)	Who should do an artificial intelligence project, how to start an artificial intelligence project. AI Frameworks/Libraries and the specialized hardware used for AI.
9)	How to effectifly integrate artificial intelligence technology into a business model
10)	The study of AI in business and society and limits of AI
11)	Bias in artificial intelligence and robustness to attack
12)	Predicting the future with AI and social impact
13)	New and emerging business areas with AI
14)	Summary, Q and A

Sources

Course Notes / Textbooks:	Jerry Kaplan, Artificial Intelligence: What Everyone Needs to Know (What Everyone Needs To Know), Oxford University Press;
References:	AI for Everyone - Coursera - Prof. Dr. Andrew Ng, Element of AI - University of Helsinki, …

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	3	% 30
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	4	56
Homework Assignments	3	6	18
Midterms	1	4	4
Final	1	4	4
Total Workload			124

Contribution of Learning Outcomes to Programme Outcomes

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

	Program Outcomes	Level of Contribution
1)	To prepare students to become communication professionals by focusing on strategic thinking, professional writing, ethical practices, and the innovative use of both traditional and new media	3
2)	To be able to explain and define problems related to the relationship between facts and phenomena in areas such as Advertising, Persuasive Communication, and Brand Management	3
3)	To critically discuss and interpret theories, concepts, methods, tools, and ideas in the field of advertising	3
4)	To be able to follow and interpret innovations in the field of advertising	1
5)	To demonstrate a scientific perspective in line with the topics they are curious about in the field.	5
6)	To address and solve the needs and problems of the field through the developed scientific perspective	3
7)	To recognize and understand all the dynamics within the field of advertising	2
8)	To analyze and develop solutions to problems encountered in the practical field of advertising	2