ECTS - Artificial Intelligence Technologies in Business and Management
Artificial Intelligence Technologies in Business and Management (ISL329) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Artificial Intelligence Technologies in Business and Management | ISL329 | Area Elective | 2 | 0 | 0 | 2.5 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | Turkish |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Demonstration, Discussion. |
| Course Lecturer(s) |
|
| Course Objectives | To enhance 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. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Knowledge and skills about applications, opportunities and risks on the Artificial Intelligence technologies and subfields of neural networks, genetic algorithms and machine learning. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction to Network Society and Digital Organizations-1 | Basic concepts about network society and digital organizations should be read. |
| 2 | Introduction to Network Society and Digital Organizations-2 | A video about digital organizations should be watched. |
| 3 | New Approaches in Digital Business and Management-1 | Articles on digital business models should be read. |
| 4 | New Approaches in Digital Business and Management-2 | Readings on digital management strategies should be completed. |
| 5 | Introduction to Artificial Intelligence Technologies-1 | Articles on the fundamentals of artificial intelligence should be read, and a brief paper should be prepared. |
| 6 | Artificial Intelligence Technologies and Applications-1 | The application areas of artificial intelligence should be researched, and a brief report should be written. |
| 7 | Artificial Intelligence Technologies and Applications-2 | Advanced readings on artificial intelligence applications should be conducted. |
| 8 | Midterm | The topics covered should be reviewed. |
| 9 | Robotics and Business Management | Articles on robotic technologies should be read. |
| 10 | Robotics and Business Management Applications | One case study on robotic applications should be conducted. |
| 11 | Neural Networks in Business Management and Applications | Readings on artificial neural networks should be completed. |
| 12 | Machine Learning in Business Management and Applications | The basics of machine learning should be researched, and key concepts should be understood. |
| 13 | Artificial Intelligence, Security, Privacy, Ethic | Recent news on artificial intelligence security and ethics should be investigated. |
| 14 | Project presentations | Preparation for project presentations should be completed. |
| 15 | Project presentations | Preparation for project presentations should be finalized. |
| 16 | Final Exam | The topics covered should be reviewed. |
Sources
| Course Book | 1. Nils J. Nilsson, Yapay Zekâ-Geçmişi ve Geleceği, Boğaziçi Üniversitesi Yayınevi, 2018. |
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Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | 15 | 16 |
| Laboratory | - | - |
| Application | 8 | 24 |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | 1 | 5 |
| Project | 1 | 10 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 20 |
| Final Exam/Final Jury | 1 | 25 |
| Toplam | 27 | 100 |
| Percentage of Semester Work | 75 |
|---|---|
| Percentage of Final Work | 25 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Acquiring the skills of understanding, explaining, and using the fundamental concepts and methods of economics | |||||
| 2 | Acquiring the skills of macro level economic analysis | |||||
| 3 | Acquiring the skills of micro level economic analysis | |||||
| 4 | Understanding the formulation and implementation of economic policies at the local, national, regional, and/or global level | |||||
| 5 | Learning different approaches on economic and related issues | |||||
| 6 | Acquiring the quantitative and/or qualitative techniques in economic analysis | |||||
| 7 | Improving the ability to use the modern software, hardware and/or technological devices | |||||
| 8 | Developing intra-disciplinary and inter-disciplinary team work skills | |||||
| 9 | Acquiring an open-minded behavior through encouraging critical analysis, discussions, and/or life-long learning | |||||
| 10 | Adopting work ethic and social responsibility | |||||
| 11 | Developing the skills of communication. | |||||
| 12 | Improving the ability to effectively implement the knowledge and skills in at least one of the following areas: economic policy, public policy, international economic relations, industrial relations, monetary and financial affairs. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | 8 | 1 | 8 |
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 14 | 1 | 14 |
| Presentation/Seminar Prepration | 1 | 2 | 2 |
| Project | 1 | 8 | 8 |
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 20 | 20 |
| Prepration of Final Exams/Final Jury | 1 | 25 | 25 |
| Total Workload | 125 | ||