| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Understands and interprets the development of the Network Society.
- Gains awareness regarding the application of developments in robotics to problems in business functions.
- Gains awareness regarding the application of developments in machine learning to problems in business functions.
- Can explain how artificial intelligence applications can enhance a company's competitive advantage.
- Gains awareness of critical issues related to legal matters, data privacy, and security regarding artificial intelligence and its applications in businesses on an international scale.
- Understands and interprets the concepts of the Network Society.
- Understands and interprets the dynamics of the Network Society.
- Gains awareness of new potential opportunities in machine learning for addressing problems in business functions.
- Gains awareness of the application of new potential opportunities to problems in business functions.
- Gains awareness of critical issues such as ethical concerns and data source privacy related to artificial intelligence and its applications in businesses on an international scale.
- Gains awareness of critical issues such as ethical concerns and data security related to artificial intelligence and its applications in businesses on an international scale.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | MAN442 INDUSTRY 4. 0
MAN443 MANAGEMENT INFORMATION SYSTEMS |
|---|
| Course Description | 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.
Students who miss more than 30% of total class hours receive an F2 grade. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to Network Society and Digital Organizations |
|
2nd Week |
Introduction to Network Society and Digital Organizations (continued) |
|
3rd Week |
New Approaches in Digital Business and Management |
|
4th Week |
New Approaches in Digital Business and Management (continued) |
|
5th Week |
Neural networks in business management and Applications |
|
6th Week |
Neural networks in business management and Applications (continued) |
|
7th Week |
Midterm |
|
8th Week |
Robotics and Business Management |
|
9th Week |
Robotics and Business Management (continued) |
|
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 (continued) |
|
| Recommended 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 Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| 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 Work Load | 158 |
| Total Work Load / 30 (h) | 5.26 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
