SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
60213YETOS-BIT2012	Artificial Intelligence for Everyone	1	Spring	2	0	2	3

Course Type :	University Elective
Cycle:	Bachelor      TQF-HE:6. Master`s Degree      QF-EHEA:First Cycle      EQF-LLL:6. Master`s Degree
Language of Instruction:	Turkish
Prerequisities and Co-requisities:	N/A
Mode of Delivery:	Face to face
Name of Coordinator:	Instructor AYŞE BERİKA VAROL MALKOÇOĞLU
Dersin Öğretim Eleman(lar)ı:
Dersin Kategorisi:	Competency Development (University Elective)

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

The aim of this course is to raise awareness about the concept of artificial intelligence, its processes and basic techniques.

Course Content:

1. Explains artificial intelligence. 2. Knows the historical development and philosophy of artificial intelligence. 3. Describes the usage areas of artificial intelligence in different disciplines. 4. Explains the analysis logic of artificial intelligence. 5. Explains the aims of artificial intelligence and the methods it uses to achieve these goals. 6. Explains social, technological and economic change with artificial intelligence. 7. Knows the relationship between artificial intelligence and ethics. 8. Express the development process of future artificial intelligence.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:

Knowledge (Described as Theoritical and/or Factual Knowledge.)

Skills (Describe as Cognitive and/or Practical Skills.)

Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	What is Artificial Intelligence? Historical Development and Philosophy
2)	Change Process with Artificial Intelligence (Profession change, social changes)
3)	Artificial Intelligence Application Areas (Natural Language processing, computer vision, decision making, problem solving, voice recognition…)
4)	How Artificial Intelligence Learns?
5)	Big Data (Data Mining, Text Mining, Learning Analytics…)
6)	Programs Used in Artificial Intelligence Applications and Application
7)	Uses and Examples of Artificial Intelligence I (Architecture)
8)	MIDTERM
9)	Uses and Examples of Artificial Intelligence II (Health)
10)	Uses and Examples of Artificial Intelligence III (Cyber Security)
11)	Uses and Examples of Artificial Intelligence IV (Art)
12)	Uses and Examples of Artificial Intelligence V (Education)
13)	Artificial Intelligence and Ethics
14)	Artificial Intelligence in the Near Future

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Recommended or Required Reading & Other Learning Resources/Tools

Course Notes / Textbooks:
References:

Level of Contribution of the Course to PLOs

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

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	Can explain the fundamental concepts, theories, and models of public relations and advertising.
2)	Can define ethical rules, legal regulations, and professional standards in the field of public relations and advertising.
3)	Can analyze the social, cultural, economic, and political contexts of public relations and advertising.
4)	Can develop public relations and advertising campaigns through target audience analysis.
5)	Can create innovative communication solutions using traditional and digital media tools.
6)	Can evaluate public relations and advertising strategies by conducting effectiveness analysis.
7)	Can take responsibility in public relations and advertising projects both individually and in team settings.
8)	Can utilize leadership and decision-making skills when determining public relations and advertising strategies.
9)	Can follow new trends and technological developments in public relations and advertising.
10)	Can generate knowledge in the field of public relations and advertising by using research and analytical skills.
11)	Can act in accordance with ethical and social responsibility principles in public relations and advertising.
12)	Can plan and implement crisis management, reputation management, and brand management processes.
13)	Can establish effective verbal and written communication in public relations and advertising processes.
14)	Can develop professional relationships in multicultural and global communication contexts.
15)	Can develop digital strategies in public relations and advertising using new media tools.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Quizzes	2	% 20.00
Midterms	1	% 30.00
Semester Final Exam	1	% 50.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE