BUSINESS ADMINISTRATION
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
COP3231	Introduction to Machine Learning for Multiple Industry Domain Applications	Fall Spring	3	0	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Hybrid
Course Coordinator :	Prof. Dr. ELİF OKAN
Recommended Optional Program Components:	None
Course Objectives:	This course aims to provide an overview of the concepts and algorithms in Machine Learning, including supervised learning and unsupervised learning. In addition to the recent approaches to overcome real world problems, an in-depth understanding of key issues will be provided. With the understanding of machine learning concepts by the participants, sector-based applications will be examined.

Learning Outcomes

The students who have succeeded in this course;
1) Gaining statistical thinking and analytical perspective
2) Understanding descriptive data analysis by uncovering hidden insights and patterns
3) Understanding basic machine learning methods to predict future trends
4) Using machine learning in practical applications and investigating its applicability
5) Checking the applicability of the machine learning methods examined
6) To have knowledge about application of machine learning in different industries
7) Having awareness about Python machine learning programming

Course Content

In addition to the new developments and applications in the field of Machine Learning, they will introduce the participants to basic principles and concepts. They will provide a solid foundation in the mathematical and statistical issues required to solve Machine Learning Problems. They will also comprehend the basic information about Python Machine Learning libraries by using some of the libraries in python (NumPy, SciPy, Pandas, Matplotlib, Scikit-learn). Students will learn basic techniques and algorithms in Machine Learning, which are widely used in today's industry level applications, and will gain experience in Machine Learning model training through applied studies that will be carried out in many fields. In addition, since students will see their Machine Learning approach in 5 different sectors, they will gain important field knowledge with the use of these algorithms in different sectors, even before they enter the industry.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to structural programming: basic programming techniques, Introduction to basic concepts such as variable, loop, conditions and function and their applications
2)	Introduction to data structures: operating logic and usage of data structures: arrays, lists, stacks, trees, maps, etc. Introduction to Object Oriented programming: Object, inheritance, encapsulation, polymorphism etc.
3)	Introduction to Machine Learning Concepts and Recent Applications in Sectors - Mathematical Basics & Statistical Thinking
4)	Exploratory Data Analysis: -Data Analysis and Visualization - Handling Data Problems, - Exploratory Data Analysis application .
5)	Machine Learning Algorithms: Overview of Machine Learning Algorithms, -Graised Learning: Classification
6)	Case Study: Finance Industry Practice
7)	Machine Learning Algorithms: - Supervised Learning: Regression Midterm
8)	Case Study: Energy Sector Practice
9)	Machine Learning Algorithms: - Community Learning Methods
10)	Case Study: Automotive Industry Practice
11)	Machine Learning Algorithms: - Model Validation
12)	Case Study: Eating & Drinking Industry Practice
13)	Machine Learning Algorithms: - Unsupervised Learning, Introduction to Artificial Neural Networks
14)	Case Study: Media Industry Practice

Sources

Course Notes / Textbooks:	• Python for Data Analysis, 2nd Edition Data Wrangling with Pandas, NumPy, and IPython, William McKinney, 2017 • Pandas for Everyone: Python Data Analysis, Daniel Y. Chen, 2017 • Building Machine Learning Systems with Python , Willi Richert, Luis Pedro Coelho , 2013 • Learning scikit-learn: Machine Learning in Python Paperback – November 25, 2013, Raúl Garreta, Guillermo Moncecchi
References:

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 20
Midterms	1	% 30
Final	1	% 50
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 30
PERCENTAGE OF FINAL WORK		% 70
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	5	14	70
Project	1	42	42
Midterms	1	2	2
Final	1	2	2
Total Workload			158

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Being able to identify problems and ask right questions	4
2)	Having problem solving skills and developing necessary analytical attitude	5
3)	Comprehending theoretical arguments along with counter arguments in detail	4
4)	Gaining awareness of lifelong learning and being qualified for pursuing graduate education	3
5)	Applying theoretical concepts in project planning	3
6)	Communicating efficiently by accepting differences and carrying out compatible teamwork	3
7)	Increasing efficiency rate in business environment	5
8)	Developing innovative and creative solutions in face of uncertainty	3
9)	Researching to gather information for understanding current threats and opportunities in business	3
10)	Being aware of the effects of globalization on society and business while deciding	5
11)	Possessing digital competence and utilizing necessary technology	5
12)	Communicating in at least one foreign language in academic and daily life	3
13)	Possessing managing skills and competence	2
14)	Deciding with the awareness of the legal and ethical consequences of business operations	2
15)	Expressing opinions that are built through critical thinking process in business and academic environment	2