Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
5İKT333Machine Learning for Economists2+0+02318.08.2026
Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Economics
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The "Machine Learning for Economists" course aims to teach the fundamentals of machine learning techniques in a way that strengthens econometric and statistical data analysis. This course will introduce machine learning methods used to understand and make predictions from data in the fields of economics and finance, and it will teach students how to apply these methods to solve real-world economic problems.
Course Content Introduction to Machine Learning
Data Preparation and Cleaning
Supervised Learning
Unsupervised Learning
Model Evaluation and Selection
Time Series Analysis and Forecasting
Machine Learning Applications and Econometric Models
Course Methods and Techniques Lecturing, Application
Prerequisites and co-requisities None
Course Coordinator Sekreter Turgay Bozan
Name of Lecturers None
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" - Aurélien Géron
Course Notes Regular attendance in the course is expected.
Students should actively participate in every stage of the course and submit assignments and projects on time.

Course Category
Social Sciences %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 15 2 30
Mid-terms 1 30 30
Final examination 1 30 30
Total Work Load   Number of ECTS Credits 3 90

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Students will understand machine learning techniques and will be able to apply basic algorithms.
2 Students will be able to analyze economic data, make predictions, and use these predictions to make economic and financial decisions.
3 Students will be able to apply data science methods by learning data preparation, model selection, and evaluation processes through practical experience.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Machine Learning
2 Data Preparing and Cleaning
3 Supervised Learning (Regression)
4 Supervised Learning (Classification)
5 Decision Trees and Support Vector Machines
6 Unsupervised Learning (Clustering)
7 Unsupervised Learning (PCA)
8 Midterm
9 Model Evaluation and Selection
10 Time Series Analysis
11 Time Series Forecasting
12 Deep Learning and AI
13 Machine Learning and Econometric Models
14 General Review
15 General Review
16 Final Exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 3 4 3 4 3 4 3 4 5 3 3 2
C1 3 4 3 4 3 4 3 4 5 3 3 3
C2 2 3 4 2 2 2 2 3 3 2 3
C3 2 2 3 3 4 3 2 3 4 3 3 1

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant