Computer Engineering (Master) (without Thesis) (English)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code:

DATS5027

Course Name:

Machine Learning Applications in Business

Semester:

Fall
Spring

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Departmental Elective

Course Level:

Master

TR-NQF-HE:7. Master`s Degree

QF-EHEA:Second Cycle

EQF-LLL:7. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator:

Doç. Dr. ŞEBNEM ÖZDEMİR

Course Lecturer(s):

Prof. Dr. DURSUN DELEN

Course Assistants:

Course Objective and Content

Course Objectives:

The purpose of this course is to provide students with a comprehensive understanding of how machine learning can be leveraged within a business setting. Students will be exposed to practical applications of machine learning algorithms and will learn to implement these techniques to solve real-world business problems. By the end of the course, students will be proficient in selecting appropriate machine learning methods for various business scenarios, optimizing model performance, and translating quantitative findings into strategic business decisions.

Course Content:

1. Overview of machine learning concepts and algorithms in a business context.
2. Data preprocessing, feature engineering, and data visualization techniques.
3. Supervised learning models for regression and classification in business decision-making.
4. Unsupervised learning for customer segmentation, market basket analysis, and anomaly detection.
5. Evaluation of machine learning models and deployment strategies for business applications.

Learning Outcomes

The students who have succeeded in this course;
1) Critically analyze business problems to identify opportunities for machine learning solutions.
2) Effectively apply data preprocessing and feature engineering to prepare datasets for machine learning.
3) Develop and tune machine learning models using contemporary software tools.
4) Interpret and communicate the results of machine learning models to a business audience.
5) Design and implement machine learning model deployment strategies within a business infrastructure.

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction to Machine Learning and its business relevance.
2)	Data collection, preprocessing, and visualization techniques.
3)	Exploring linear regression models and their business applications.
4)	Classification models for customer prediction and retention strategies.
5)	Decision trees and ensemble methods for complex business decisions.
6)	Unsupervised learning techniques for market segmentation.
7)	Model selection, cross-validation, and hyperparameter tuning.
8)	Midterm Exam.
9)	Neural networks and deep learning in business applications.
10)	Natural Language Processing (NLP) for customer feedback analysis.
11)	Reinforcement learning for dynamic business strategy development.
12)	Model evaluation metrics and model interpretability.
13)	Ethical considerations and bias mitigation in business machine learning.
14)	Deployment of models into production, monitoring, and maintenance.
15)	Case studies of successful machine learning applications in business.
16)	Final Exam.

Sources

Course Notes / Textbooks:	Herhangi bir ders kitabı bulunmamaktadır. There is no textbook.
References:	Güncel makaleler, kitaplar kullanılacaktır. Current articles and books will be used.

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5
Program Outcomes
1) Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications.
2) To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
3) To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines.
4) To be able to solve the problems encountered in the field by using research methods.
5) Being able to independently carry out a work that requires expertise in the field.
6) To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility.
7) To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.
8) To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data.
9) To be able to communicate orally and in writing using a foreign language at least at the B2 General Level of the European Language Portfolio.
10) To be able to use information and communication technologies at an advanced level along with computer software at the level required by the field.
11) To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field.
12) To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications.
2)	To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
3)	To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines.
4)	To be able to solve the problems encountered in the field by using research methods.
5)	Being able to independently carry out a work that requires expertise in the field.
6)	To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility.
7)	To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.
8)	To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data.
9)	To be able to communicate orally and in writing using a foreign language at least at the B2 General Level of the European Language Portfolio.
10)	To be able to use information and communication technologies at an advanced level along with computer software at the level required by the field.
11)	To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field.
12)	To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Preparation for the Activity	Spent for the Activity Itself	Completing the Activity Requirements	Workload
Course Hours	14	0	3		42
Midterms	1	40	1		41
Final	1	60	1		61
Total Workload					144