TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Bachelor's Degree
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	5
NAME OF LECTURER(S)	-
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Understand and interpret the concepts, development and dynamics of Network Society. 2) Aware of potential opportunities and risks of robotics applications in business functions. 3) Aware of potential opportunities and risks of machine learning applications in business functions. 4) Explain how artificial intelligence practices can help the enterprise increase its competitive advantage. 5) Aware of the critical issues related to the legal and ethical issues, the confidentiality and security of the data sources, etc. in the field of artificial intelligence and applications in enterprises. 6) 7) 8) 9) 10) 11)
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	MAN442 INDUSTRY 4. 0 MAN443 MANAGEMENT INFORMATION SYSTEMS
COURSE DEFINITION	The aim of this course is to enhance the students' understanding, knowledge and skills about applications, opportunities and risks on the Artificial Intelligence technologies and subfields of neural networks, genetic algorithms and machine learning in business management. Besides, it is aimed that students have information about security, ethics and social issues in digitalized enterprises.
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction to Network Society and Digital Organizations 2nd Week Introduction to Network Society and Digital Organizations 3rd Week New Approaches in Digital Business and Management 4th Week New Approaches in Digital Business and Management 5th Week Neural networks in business management and Applications 6th Week Neural networks in business management and Applications 7th Week Robotics and Business Management 8th Week Midterm 9th Week Robotics and Business Management 10th Week Neural networks in business management and Applications 11th Week Machine learning in business management and Applications 12th Week Artificial Intelligence, Security, Privacy, Ethics 13th Week Project presentations 14th Week Project presentations
RECOMENDED OR REQUIRED READING	Nils J. Nilsson, Yapay Zeka-Geçmişi ve Geleceği, Boğaziçi Üniversitesi Yayınevi, 2018. Mick Benson, Artificial Intelligence: Concepts and Applications , Willford Press (May 16, 2018). Jim Sterne, Artificial Intelligence for Marketing: Practical Applications, Wiley. Akerkar, Rajendra Artificial Intelligence for Business, 2019. https://www.springer.com/us/book/9783319974354.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Case Study,Project,Presentation
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 25 Project 1 30 Other 1 10 Total(%) 65 Contribution of In-term Studies to Overall Grade(%) 65 Contribution of Final Examination to Overall Grade(%) 35 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 2 2 Preparation for Quiz Individual or group work 14 1 14 Preparation for Final exam 1 40 40 Course hours 14 3 42 Preparation for Midterm exam 1 55 55 Laboratory (including preparation) Final exam 1 2 2 Homework 1 3 3 Total Workload 158 Total Workload / 30 5,26 ECTS Credits of the Course 5
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No