School/Faculty/Institute | Faculty of Economics, Administrative 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 |
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Estimated Student Workload | 135 hours per semester | ||||
Number of Credits | 5 ECTS | ||||
Grading Mode | Standard Letter Grade | ||||
Pre-requisites |
ECON 337 - R Programming for Social Sciences |
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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 CompetencesUpon 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. |
Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
---|---|---|---|---|
1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | ||||
2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | ||||
3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | ||||
4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | ||||
5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | ||||
6) Internalization and dissemination of professional ethical standards. | ||||
7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | ||||
8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | ||||
9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | ||||
10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | ||||
11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | ||||
12) Ability to acquire knowledge independently, and to plan one’s own learning. | ||||
13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
N None | S Supportive | H Highly Related |
Program Outcomes and Competences | Level | Assessed by | |
1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,HW,Participation |
4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
6) | Internalization and dissemination of professional ethical standards. | N | |
7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Participation |
10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | HW,Participation |
11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,HW |
13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,HW |
Prepared by and Date | NAROD ERKOL , February 2024 |
Course Coordinator | MUHAMMED ABDULLAH ALTUNDAL |
Semester | Spring |
Name of Instructor | Asst. Prof. Dr. NAROD ERKOL |
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 |
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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. |
Activity | No/Weeks | Calculation | |||
No/Weeks per Semester | |||||
Laboratory | 24 | 120 | |||
Homework Assignments | 6 | 72 | |||
Midterm(s) | 2 | 34 | |||
Final Examination | 2 | 44 | |||
Total Workload | 270 | ||||
Total Workload/25 | 10.8 | ||||
ECTS | 5 |