ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
YBS414	ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING	5	3	3	5

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Type of the Course :	Elective
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Assist.Prof. NİSA GÜLENER YILDIRIM
Instructor(s) of the Course Unit
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	The aim of the course is to provide an introduction to the current status of Artificial Intelligence and Machine Learning.
Contents of the Course Unit:	The content of the course is to create awareness in the field of Artificial Intelligence and Machine Learning and to learn algorithms with examples on current topics.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Defines the concepts of artificial intelligence and machine learning. Discovers current topics and content in this field. Examines how a business can develop projects in this field. Masters the algorithms in its field. Can take part in studies in this field.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	Literature Reading, Current Examples	Artificial Intelligence, Introduction to Python	Explanation, Discussion, Application
2	Literature Reading, Current Examples	Machine Learning, Data and Data Preprocessing Concepts	Explanation, Discussion, Application
3	Literature Reading, Current Examples	Data Preprocessing Concepts and Python	Explanation, Discussion, Application
4	Literature Reading, Current Examples	Data Preprocessing with Python (Seaborn Library)	Explanation, Discussion, Application
5	Literature Reading, Current Examples	Data Preprocessing and Visualization with Python (Pandas and MatplotLib Libraries)	Explanation, Discussion, Application
6	Literature Reading, Current Examples	Linear Regression	Explanation, Discussion, Application
7	Literature Reading, Current Examples	Linear Regression (coding)	Explanation, Discussion, Application
8	-	MID-TERM EXAM	-
9	Literature Reading, Current Examples	Decision Trees	Explanation, Discussion, Application
10	Literature Reading, Current Examples	Decision Trees (coding)	Explanation, Discussion, Application
11	Literature Reading, Current Examples	Random Forest	Explanation, Discussion, Application
12	Literature Reading, Current Examples	Logistic Regression	Explanation, Discussion, Application
13	Literature Reading, Current Examples	Support vector machine	Explanation, Discussion, Application
14	Literature Reading, Current Examples	Artificial Neural Networks	Explanation, Discussion, Application
15	Literature Reading, Current Examples	Artificial Neural Networks	Explanation, Discussion, Application
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Makine öğrenmesi / Ethem Alpaydın Veri madenciliği ve makine öğrenmesi : temel kavramlar, algoritmalar, uygulamalar / editörler. M. Erdal Balaban, Elif Kartal.

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method
Mid-Term Exam	1	50
Final Exam	1	50
TOTAL	2	100

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Define the basic concepts of economics, administrative and human sciences.
2	Summarize the basic communication theories with information about the field of Radio Television and Cinema.
3	Communicate and explain the current information about communication with communication theories.

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Relate the knowledge and facts in the field of communication with other areas of social sciences such as law, philosophy, economics, politics, and sociology.
2	Explain the structure and operation of the radio, television and cinema industries and use this information in their professional life.

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Using the data sources published in his / her field effectively, he /she conduct research and use the results in his / her studies.
2	Analyze the international media structures and choose the media structure that is appropriate for his/her community.

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Analyze and evaluate social events at national and international level through media practices.

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Apply his / her knowledge about media management and organization in his / her professional life.
2	Propose individual or team solutions for any problems in professional applications and production.

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Follow the developments in the field of communication, the studies related to the field and the mass media tools, and critically discuss the acquired knowledge.
2	Critically interpret the economic, political and sociological dimensions of the media.
3	Recognize universal norms of law and evaluate existing structures in the field of communication in the context of communication law and communication ethics and apply these gains throughout life.

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Conduct literature research in a foreign language related to his/her field.
2	Prepare presentations for the film festival in Turkey and abroad, media organizations and communication symposia by using the communication skills.

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Defend scientific, social and ethical values in his / her field studies.
2	Create film history, script, fiction, audio and video techniques knowledge and designs short or feature film stages.

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	14	3	42
Preliminary & Further Study	14	1	14
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	0	0	0
Assignment (Homework)	7	7	49
Project Work	1	14	14
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	0	0	0
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	1	1	1
Preparation for the Final Exam	2	2	4
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	0	0	0
Short Exam	2	2	4
Preparation for the Short Exam	0	0	0
TOTAL	42	0	129