BAL609 - INTRODUCTION TO MACHINE LEARNING
Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
---|---|---|---|---|---|---|
INTRODUCTION TO MACHINE LEARNING | BAL609 | 1st Semester | 3 | 0 | 3 | 6 |
Prequisites | ||||||
Course language | Turkish | |||||
Course type | Must | |||||
Mode of Delivery | Face-to-Face | |||||
Learning and teaching strategies | Lecture | |||||
Instructor (s) | Instructors of Department of Business Administration | |||||
Course objective | The objective of this course is to establish a background on machine learning. | |||||
Learning outcomes |
| |||||
Course Content | Basic information on machine learning; supervised and unsupervised machine learning techniques; Neural Networks; coding studies on machine learning. | |||||
References | Alpaydın, E. (2014). Introduction to Machine Learning (Vol 2). The MIT Press Mitchell, T. (1997) Machine Learning (Vol 1). McGraw-Hill Education |
Course outline weekly
Weeks | Topics |
---|---|
Week 1 | What Is Machine Learning |
Week 2 | Input-Output Functions |
Week 3 | Logical Functions |
Week 4 | Supervised and Unsupervised Learning |
Week 5 | Statistical Learning |
Week 6 | Computational Learning |
Week 7 | Clustering Techniques |
Week 8 | Midterm |
Week 9 | Neural Networks - Threshold Logic Unit |
Week 10 | Neural Networks - Linear Machines |
Week 11 | Neural Networks - TLU Networks |
Week 12 | Exemplary Coding Studies |
Week 13 | Exemplary Coding Studies |
Week 14 | Exemplary Coding Studies |
Week 15 | Review |
Week 16 | Final Exam |
Assesment methods
Course activities | Number | Percentage |
---|---|---|
Attendance | ||
Laboratory | ||
Application | ||
Field activities | ||
Specific practical training | ||
Assignments | ||
Presentation | ||
Project | ||
Seminar | ||
Midterms | ||
Final exam | ||
Total | ||
Percentage of semester activities contributing grade succes | ||
Percentage of final exam contributing grade succes | ||
Total |
WORKLOAD AND ECTS CALCULATION
Activities | Number | Duration (hour) | Total Work Load |
---|---|---|---|
Course Duration (x14) | 0 | ||
Laboratory | 0 | ||
Application | 0 | ||
Specific practical training | 0 | ||
Field activities | 0 | ||
Study Hours Out of Class (Preliminary work, reinforcement, ect) | 0 | ||
Presentation / Seminar Preparation | 0 | ||
Project | 0 | ||
Homework assignment | 0 | ||
Midterms (Study duration) | 0 | ||
Final Exam (Study duration) | 0 | ||
Total Workload | 0 | 0 | 0 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
D.9. Key Learning Outcomes | Contrubition level* | ||||
---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |
1. Conducts novel and ethical research on business, reports outcomes in a critical manner. | X | ||||
2. Solves problems via appropriate softwares, adapts to new methods and software. | X | ||||
3. Has managerial and leadership skills to identify problems, objectives and strategic plans for organizational progress with a critical point of view. | X | ||||
4. Plays an active role in projects, analyses relationship between stakeholders accurately, motivates and manages all stakeholders through effective language skills. | X | ||||
5. Has necessary communication skills to manage verbal and written communication. | X | ||||
6. Analyses and uses contemporary and advanced knowledge in relation with information from different areas. | X | ||||
7. Progresses continuously and transfers the experience in both written and verbal ways. | X | ||||
8. Through anticipation and strategic thinking, plays an active role in organizational decision making process. | X | ||||
9. Uses knowledge in consistency with the ethical, social and international values in an unbiased manner. | X | ||||
10. Has expertise on the multi-disciplinary nature of management and related fields. | X | ||||
11. Approaches problems with a wide strategic perspective, self-develops continuously. | X | ||||
12. Shares novel studies, is up to date both in knowledge and personal network. | X |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest