Course Details

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
3DSBE520MACHINE LEARNING IN BUSINESS AND ECONOMICS3+0+037,513.05.2025

 
Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program DATA SCIENCE
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Introducing unsupervised and supervised learning algorithms.
Illustrating implementations of machine learning techniques into problems found in
business and economics fields.
Familiarizing students with the processes needed to develop, report and analyze big
business data.
Course Content The course introduces the field of machine learning to graduate students of business,
economics and similar backgrounds. Students learn about the theoretical foundations
of machine learning and ways to apply machine learning principles to solving new
problems. The content is designed assuming no prior knowledge in machine learning
and it includes two major paradigms in machine learning which are supervised and
unsupervised learning. In supervised learning sections, students are introduced to
various methods for classification and regression. In unsupervised learning sections,
dimensionality reduction and clustering are discussed.
Course Methods and Techniques The course covers key machine learning techniques including supervised learning algorithms (such as linear regression, decision trees, and neural networks) and unsupervised learning methods (like clustering and dimensionality reduction). It emphasizes essential practices like feature engineering, data preprocessing, and model evaluation using techniques such as cross-validation. Students will gain hands-on experience in applying these methods to business and economics problems, developing the skills to analyze big business data effectively.
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Associate Prof.Dr. Selen Madenoğlu
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources -
Course Notes "Machine Learning for Business: Using Amazon SageMaker and AWS" by Doug Hudgeon and Richard Nichol

Course Category
Engineering %30
Social Sciences %70

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
Yarıl yılSonu Sınavı/Dönem Projesinin Başarı Notuna Katkısı 2 % 20
Ödev 7 % 20
Sunum/Seminer 1 % 10
Final examination 1 % 50
Total
11
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Grup Projesi 2 20 40
Ev Ödevi 7 3 21
Proje 2 20 40
Kişisel Çalışma 1 40 40
Yüz Yüze Ders 14 3 42
Final Sınavı 1 40 40
Total Work Load   Number of ECTS Credits 7,5 223

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Interpret the distinction between supervised and unsupervised learning, and the interests and difficulties of both approaches.
2 Denetimli öğrenmeden temel algoritmalarını, örneğin lineer regresyon ve sınıflandırmayı tanımlayabilmek.
3 Interpret neural networks algorithm and the relation between training and generalization.
4 Interpret main algorithms from unsupervised learning including clustering algorithms.
5 Conduct original research on a personal project related to machine learning implementing an innovative new approach for solving questions related to this problem.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Makine Öğrenmesine Giriş Introduction to Machine Learning
2 Denetimli ve Denetimsiz Öğrenmenin Karşılaştırılması Supervised vs. Unsupervised Learning
3 Veri Ön İşleme ve Özellik Mühendisliği Data Preprocessing and Feature Engineering
4 Linear Regression and Business Applications
5 Classification Algorithms: Logistic Regression, k-NN
6 Decision Trees and Random Forests
7 Sinir Ağları ve Derin Öğrenmeye Giriş
8 Midterm Exam / Project Proposal
9 Unsupervised Learning: Clustering Algorithms
10 Dimensionality Reduction: PCA, t-SNE
11 Model Evaluation and Cross-Validation
12 Overfitting, Bias-Variance Tradeoff
13 Big Data and Machine Learning in Economics
14 Student Presentations & Feedback

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
C1 3 5 4 2 1 1 2
C2 3 5 4 2 1 1 2
C3 3 5 4 2 1 1 2
C4 3 5 4 2 1 1 2
C5 4 5 5 4 5 5 4 5 5 5

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

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=75106&lang=en
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