Bayburt University Lifelong Learning Programme

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type of Course Unit	Year of Study	Semester	Number of ECTS Credits
İK122	Machine Learning in Economics	Elective	1	2	6

Level of Course Unit

Second Cycle

Objectives of the Course

The aim of this course is to provide an introductory practical introduction to the basic machine learning algorithms that are widely used in economic analysis today.

Name of Lecturer(s)

Doç. Dr. Erdemalp Özden

Learning Outcomes

1	Understand the basics of machine learning methods widely used in economic and social applications.
2	Develop the necessary programming skills to apply these methods to practical problems.
3	To be able to follow the recent literature on machine learning in social sciences
4	Develop the ability to design a project in a data-rich environment
5	Developing skills on how machine learning methods can be applied in decision-making processes

Mode of Delivery

Normal Education

Prerequisites and co-requisities

Introduction to Econometrics

Recommended Optional Programme Components

Basic Programming Knowledge Intermediate Algebra

Course Contents

This course will cover the use of machine learning techniques to solve problems in economics and other related social sciences. R and Python will be used for the applications in this course. The course content covers both supervised and unsupervised learning techniques. Major topics include: Regression analysis, regularization methods, LASSO and Ridge regression, logistic regression, decision trees, classification methods. .

Weekly Detailed Course Contents

Week	Theoretical	Practice	Laboratory
1	Introduction to machine learning methods in economics, basic concepts and tools in learning theory
2	Supervised and unsupervised learning, estimation error, loss function, cross-validation, data-based information measures
3	Introduction to programming with R, summary statistical analysis with R
4	Introduction to programming with Python, summary statistical analysis with Python
5	Introduction to supervised learning: Linear Regression
6	Classification, logistic regression, PCA; discriminant analysis
7	Resampling methods, deviation-variance relationship, cross validation, data-based information criteria
8	Midterm Exam
9	Model selection and regularization: shrinkage, LASSO, ridge regression
10	Nonlinear regression, polynomial regression
11	Regression trees
12	Support vector machines
13	Unsupervised Learning: PCA, K-means grouping
14	Project presentations
15	Final

Recommended or Required Reading

James, Gareth, D. Witten, T. Hastie, R. Tibshirani (2017), An Introduction to Statistical Learning with Applications in R, 8th ed., Springer. Alpaydın, Ethem (2018), Artificial Learning, 4th edition (translation from Ethem Alpaydın, Introduction to Machine Learning, 2nd edition), Boğaziçi University Publishing House, Istanbul.

Planned Learning Activities and Teaching Methods

Assessment Methods and Criteria

Term (or Year) Learning Activities	Quantity	Weight
Midterm Examination	1	100
SUM		100
End Of Term (or Year) Learning Activities	Quantity	Weight
Final Examination	1	100
SUM		100
Term (or Year) Learning Activities		30
End Of Term (or Year) Learning Activities		70
SUM		100

Language of Instruction

Turkish

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Midterm Examination	1	2	2
Final Examination	1	2	2
Attending Lectures	14	3	42
Report Preparation	2	5	10
Report Presentation	2	2	4
Individual Study for Homework Problems	5	5	25
Individual Study for Mid term Examination	7	3	21
Individual Study for Final Examination	7	6	42
Homework	5	5	25
TOTAL WORKLOAD (hours)			173

Contribution of Learning Outcomes to Programme Outcomes

	PO 1	PO 2	PO 3	PO 4	PO 5	PO 6
LO1	4	4	4	5	1	2
LO2	4	3	4	3	3	2
LO3	4	4	3	2	1	2
LO4	5	3	4	2	2	1
LO5	4	4	4	2	3	2

* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High