| School/Faculty/Institute |
Faculty of Econ., Admin. and Social Sciences |
| Course Code |
ECON 442 |
| Course Title in English |
Machine Learning in Decision Making |
| Course Title in Turkish |
Karar Verme Sürecinde Makine Öğrenimi Yöntemleri |
| Language of Instruction |
EN |
| Type of Course |
Laboratory Work |
| Level of Course |
Select |
| Semester |
Spring |
| Contact Hours per Week |
| Lecture: 0 |
Recitation: 0 |
Lab: 3 |
Other: 0 |
|
| Estimated Student Workload |
135 hours per semester |
| Number of Credits |
5 ECTS |
| Grading Mode |
Standard Letter Grade
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| Pre-requisites |
ECON 337 - R Programming for Social Sciences
|
| Expected Prior Knowledge |
Basics of R programming and statistics |
| Co-requisites |
None |
| Registration Restrictions |
ECON 337 |
| Overall Educational Objective |
Learn the basics of Machine Learning tools and basic data-handling procedures.
|
| Course Description |
In this course, students will learn how to solve business problems effectively by using machine learning. This course is an introductory level course. |
| Course Description in Turkish |
Bu derste öğrenciler, makine öğrenimini kullanarak iş problemlerini etkili bir şekilde çözmeyi öğreneceklerdir. Bu ders başlangıç seviyesinde bir derstir. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Understand fundamentals of R programming language of ML tools
2) Analyze data by using visual methods
3) Understand fundamentals concepts of machine learning
4) Model building and validation using tree based machine learning models.
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| Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
4 |
| 1) Has a broad understanding of economics with a deep exposure to other social sciences and mathematics. |
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| 2) Demonstrates knowledge and skills in understanding the interactions of different areas of economics. |
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| 3) Displays a sound comprehension of microeconomic and macroeconomic theory.
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| 4) Applies economic concepts to solve complex problems and enhance decision-making capability. |
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| 5) Uses quantitative techniques to analyze different economic systems.
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| 6) Applies theoretical knowledge to analyze issues regarding Turkish and global economies. |
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| 7) Demonstrates proficiency in statistical tools and mainstream software programs to process and evaluate economic data. |
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| 8) Behaves according to scientific and ethical values at all stages of economic analysis: data collection, interpretation and dissemination of findings.
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| 9) Uses written and spoken English effectively (at least CEFR B2 level) to exchange scientific information. |
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| 10) Exhibits individual and professional ethical behavior and social responsibility. |
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| 11) Displays learning skills necessary for further study with a high degree of autonomy |
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Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
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Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
Has a broad understanding of economics with a deep exposure to other social sciences and mathematics. |
S |
|
| 2) |
Demonstrates knowledge and skills in understanding the interactions of different areas of economics. |
N |
|
| 3) |
Displays a sound comprehension of microeconomic and macroeconomic theory.
|
N |
|
| 4) |
Applies economic concepts to solve complex problems and enhance decision-making capability. |
H |
|
| 5) |
Uses quantitative techniques to analyze different economic systems.
|
H |
|
| 6) |
Applies theoretical knowledge to analyze issues regarding Turkish and global economies. |
N |
|
| 7) |
Demonstrates proficiency in statistical tools and mainstream software programs to process and evaluate economic data. |
H |
|
| 8) |
Behaves according to scientific and ethical values at all stages of economic analysis: data collection, interpretation and dissemination of findings.
|
S |
|
| 9) |
Uses written and spoken English effectively (at least CEFR B2 level) to exchange scientific information. |
S |
|
| 10) |
Exhibits individual and professional ethical behavior and social responsibility. |
S |
|
| 11) |
Displays learning skills necessary for further study with a high degree of autonomy |
S |
|
| Prepared by and Date |
NAROD ERKOL , February 2024 |
| Course Coordinator |
NAROD ERKOL |
| Semester |
Spring |
| Name of Instructor |
Asst. Prof. Dr. NAROD ERKOL |
Course Contents
| Week |
Subject |
| 1) |
Introduction to Machine Learning and R |
| 2) |
Data Structures I: Vectors, Matrices |
| 3) |
Data Structures I: Data Frames, Lists, Loops |
| 4) |
Data Visualization |
| 5) |
Building Linear Regression & Classification Models |
| 6) |
Linear Model Selection |
| 7) |
Midterm exam |
| 8) |
Fundamental concepts in Machine Learning: Bias/Variance tradeoff and Cross-Validation |
| 9) |
Model Building and Validation in decision tree models and bagging |
| 10) |
Model Building and Validation in decision tree models and bagging |
| 11) |
Model Building and Validation in random forest models and boosting |
| 12) |
Model Building and Validation in Support Vector Machines |
| 13) |
Machine learning application: predicting sales and demand |
| 14) |
Machine learning application: improving marketing strategy |
| 15) |
Final exam |
| Required/Recommended Readings | Tibshirani, R., James, G., Witten, D., & Hastie, T. (2013). An Introduction to Statistical Learning with Applications in R. New York: Springer. |
| Teaching Methods | Active Learning
Flipped Learning |
| Homework and Projects | Pre class and in class assignments |
| Laboratory Work | Yes |
| Computer Use | Yes |
| Other Activities | NA |
| Assessment Methods |
| Assessment Tools |
Count |
Weight |
| Homework Assignments |
4 |
% 40 |
| Project |
1 |
% 30 |
| Final Examination |
1 |
% 30 |
| TOTAL |
% 100 |
|
| Course Administration |
erkoln@mef.edu.tr
02123953670
Course Instructor: Asst. Prof. Narod Erkol (erkoln@mef.edu.tr)
Attendance/participation: Students are expected to prepare for the lecture via pre-class assignments, videos and reading materials. Students are responsible to follow the announcements, course materials available on Blackboard system.
Formal use of e-mails: Students are expected to use their @mef accounts for email traffic. The instructor is only responsible for the information sent/received through Blackboard system and emails using @mef account. The course instructor assumes that any information sent through email will be received in 24 hours, unless a system problem occurs.
Grading and evaluation: Evaluation will be based on the student learning outcomes. It is strongly recommended to complete all the work in a timely fashion. Late submissions will not be accepted.
Missing projects: No make up unless a legitimate proof of absence is presented.
Missing final exam: Faculty regulations.
Academic integrity: All students of MEF University are expected to be honest and comply with academic integrity. Students are expected to do their own work and neither give nor receive unauthorized assistance. Disciplinary action will be taken in case of suspicion.
Improper behavior, academic dishonesty and plagiarism: Law on Higher Education Article 54.
Important: If the learner cannot collect at least 30 points from the activities other than the final exam, they can not take the final exam and will get an FZ grade. |
Information Package / Course Catalogue