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Information
| Unit | INSTITUTE OF SOCIAL SCIENCES |
| ECONOMETRICS (MASTER) (WITH THESIS) | |
| Code | IEM751 |
| Name | Machine Learning |
| Term | 2025-2026 Academic Year |
| Term | Fall and Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi Salih ÇAM |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
It aims to visualize, analyze and interpret structured or semi-structured data.
Course Content
The course includes data visualization, linear regression models, models with limited dependent variables, artificial neural network algorithms, Support Vector Machine (SVM), Nearest K-Neighbor algorithm and decision trees.
Course Precondition
No prerequisites are required.
Resources
KOÇ, D. T. (Ed.). (2022). Ekonomide Veri Bilimi, Makine Öğrenmesi, Derin Öğrenme ve Uygulamaları. Akademisyen Kitabevi. Özdemir, M. (2020). R ile programlama ve makine öğrenmesi. Nobel Akademik Yayıncılık.
Notes
Lecture notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Knows basic data structures. |
| LO02 | Visualizes the data using an appropriate method |
| LO03 | Determines the appropriate model to predict variables. |
| LO04 | Estimates the corresponding model with econometrics software. |
| LO05 | Identifies and corrects possible problems in the analysis. |
| LO06 | interprets the outcomes obtained by a machine learning algorithm. |
| LO07 | Makes inferences on the basis of model output. |
| LO08 | Makes prediction based on the utilized model. |
| LO09 | Evaluates the forecasting process and improves the model. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explains contemporary concepts about Econometrics, Statistics, and Operation Research | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Explains relationships between acquired knowledge about Econometrics, Statistics, and Operation Research | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Explains how to apply acquired knowledge in the field to Economics, Business, and other social sciences | 5 |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Performs conceptual analysis to develop solutions to problems | 4 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Models problems with Mathematics, Statistics, and Econometrics | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Interprets the results obtained from the most appropriate method to predict the model | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Synthesizes the information obtained by using different sources within the framework of academic rules in a field of research | 3 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Uses acquired knowledge in the field to determine the vision, aim, and goals for an organization/institution | |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Searches for new approaches and methods to solve problems being faced | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Presents analysis results conveniently | 5 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Collects/analyzes data in a purposeful way | 5 |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Converts its findings into a master's thesis or a professional report in Turkish or a foreign language | 4 |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Develops solutions for organizations using Econometrics, Statistics, and Operation Research | 4 |
| PLO14 | Beceriler - Bilişsel, Uygulamalı | Uses a package program/writes a new code for Econometrics, Statistics, and Operation Research | 3 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research | |
| PLO16 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads by taking responsibility individually and/or within the team | |
| PLO17 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the necessity of lifelong learning, it constantly renews itself by following the current developments in the field of study | |
| PLO18 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Interprets the feelings, thoughts and behaviors of the related persons correctly/expresses himself/herself correctly in written and verbal form | |
| PLO19 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets data on economic and social events by following current issues | |
| PLO20 | Yetkinlikler - Alana Özgü Yetkinlik | Applies social, scientific and professional ethical values |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction of big data and data structures | Reading about the topic | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 2 | Introduction to machine learning and basic algorithms used in machine learning | Reading about the topic | Öğretim Yöntemleri: Tartışma, Soru-Cevap, Anlatım |
| 3 | Supervised learning | Reading about the topic | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | Unsupervised learning | Reading about the topic | Öğretim Yöntemleri: Anlatım, Tartışma, Alıştırma ve Uygulama |
| 5 | Reinforcement Learning | Reading about the topic | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Linear regression models and its assumptions. | Reading about the topic and data preparation. | Öğretim Yöntemleri: Anlatım, Tartışma, Alıştırma ve Uygulama |
| 7 | Deviations from the assumptions of the linear regression model | Reading about the topic | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Örnek Olay |
| 8 | Mid-Term Exam | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Models with restricted dependent variables | Reading about the topic | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Örnek Olay |
| 10 | Support Vector Machines (SVM) | Reading about the topic | Öğretim Yöntemleri: Anlatım, Örnek Olay, Alıştırma ve Uygulama |
| 11 | K nearest neighbor model | Reading about the topic | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Principal component analysis | Preparing an appropriate data set | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 13 | Introduction to artificial neural network algorithms, the basic applications of artificial neural network algorithm and main function types | Preparing an appropriate data set to forecast | Öğretim Yöntemleri: Alıştırma ve Uygulama, Anlatım |
| 14 | Estimation and interpretation of artificial neural network algorithm with supervised learning | Estimating the model | Öğretim Yöntemleri: Bireysel Çalışma, Proje Temelli Öğrenme |
| 15 | estimation and interpretation of artificial neural networks with unsupervised learning | Estimating the model | Öğretim Yöntemleri: Bireysel Çalışma, Proje Temelli Öğrenme |
| 16 | Term Exams | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav, Proje / Tasarım, Performans Değerlendirmesi |
| 17 | Term Exams | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav, Proje / Tasarım, Performans Değerlendirmesi |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 6 | 84 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 3 | 5 | 15 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 5 | 5 |
| Final Exam | 1 | 4 | 4 |
| Total Workload (Hour) | 150 | ||
| Total Workload / 25 (h) | 6,00 | ||
| ECTS | 6 ECTS | ||