Aircraft Technology
Associate	TR-NQF-HE: Level 5	QF-EHEA: Short Cycle	EQF-LLL: Level 5

Course Introduction and Application Information

Course Code:

UNI220

Course Name:

Machine Learning and Data Science

Semester:

Fall

Course Credits:

ECTS

Language of instruction:

Turkish

Course Condition:

Does the Course Require Work Experience?:

Type of course:

University Elective

Course Level:

Associate

TR-NQF-HE:5. Master`s Degree

QF-EHEA:Short Cycle

EQF-LLL:5. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator:

Dr. Öğr. Üy. ALPER ÖNER

Course Lecturer(s):

Ferzat Anka

Course Assistants:

Course Objective and Content

Course Objectives:	The aim of the course is to provide students with information on basic techniques and methods in artificial learning and to enable students to have the ability to use artificial learning methods in solving practical problems. At the same time, it is to understand the importance of machine learning in today's application areas.
Course Content:	Machine learning basic concepts and methods. Problem solving using machine learning; methods using and not using problem information. Data analysis, To examine various algorithms. To explain the importance of artificial intelligence methods in different fields with examples

Learning Outcomes

The students who have succeeded in this course;
1) • Recognize the problems that can be solved by machine learning methods.
2) • Understanding the importance of artificial intelligence in solving various problems
3) • Can choose the appropriate machine learning method for the given problem.
4) • Can solve the given problem with the appropriate machine learning method.
5) • Knows the ways of representing information, its advantages and disadvantages.

Course Flow Plan

Week	Subject	Related Preparation
1)	Machine learning history and philosophy
2)	Basic concepts
3)	Basic concepts-Intelligent Agents
4)	Introduction to machine learning and problem solving and search algorithms
5)	Expert systems and machine learning
6)	Optimization methods in machine learning
7)	Homework-Presentation
8)	Homework-Presentation
9)	Homework-Presentation
10)	Data science and analysis
11)	Machine learning
12)	Data science and methods
13)	Machine learning
14)	Search algorithms and their importance (Definite, greedy, heuristic, meta-heuristic)

Sources

Course Notes / Textbooks:

• Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010,
• Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition
• Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi 4. Baskı
• Yalçin Özkan, Veri Madenciliği Yöntemleri, Papatya, 2008
• Cemalettin Kubat, Matlab Yapay Zeka ve Mühendislik uygulamaları, Pusula, 2009
• İlker Arslan, R ile İstatistiksel Programlama, Pusula, 2020
• Zafer Demirkol, Herkes İçin Yapay Zeka, Genç Destek, 2021
• S.Nematzadeh et al. Rationalized Statistics for Biosciences Analysing bioinformatics data using the R, LAP Publishing, 2021

References:

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5
Program Outcomes
1) To have theoretical and practical knowledge at the basic level supported by textbooks, application tools and other sources with current knowledge in the field based on the qualifications gained at the secondary level.
2) To gain the ability to use the theoretical and practical knowledge at the basic level in the same field at an advanced level or at the same level. To be able to interpret and evaluate data, to define problems, to analyze and to develop solutions based on evidence, by using basic knowledge and skills acquired in the field.
3) -To be able to transfer his / her thoughts at the level of basic knowledge and skills related to his / her field through written and verbal communication. -To be able to share his / her thoughts and solutions to the problems related to his / her field with experts and non-experts. -To be able to follow information in his / her field and communicate with his / her colleagues by using a foreign language at least at a level of European Language Portfolio A2 General Level. -To be able to use information and communication technologies together with computer software at the basic level of European Computer Driving License required by the field.
4) -To be able to evaluate the basic knowledge and skills acquired in the field with a critical approach, to determine and meet the learning needs. -To be able to direct his / her education to an advanced education level in the same field or to a profession at the same level. -Gaining consciousness of lifelong learning.
5) -To have social, scientific, cultural and ethical values in the stages of gathering, applying and announcing the results related to the field. -To have sufficient consciousness about the universality of social rights, social justice, quality and cultural values, environmental protection, occupational health and safety.
6) -To be able to conduct a basic level study independently. -To be able to take responsibility as a team member to solve unforeseen complex problems encountered in the applications related to the field. -To be able to carry out activities for the development of employees working under their responsibility within the framework of a project.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	To have theoretical and practical knowledge at the basic level supported by textbooks, application tools and other sources with current knowledge in the field based on the qualifications gained at the secondary level.
2)	To gain the ability to use the theoretical and practical knowledge at the basic level in the same field at an advanced level or at the same level. To be able to interpret and evaluate data, to define problems, to analyze and to develop solutions based on evidence, by using basic knowledge and skills acquired in the field.
3)	-To be able to transfer his / her thoughts at the level of basic knowledge and skills related to his / her field through written and verbal communication. -To be able to share his / her thoughts and solutions to the problems related to his / her field with experts and non-experts. -To be able to follow information in his / her field and communicate with his / her colleagues by using a foreign language at least at a level of European Language Portfolio A2 General Level. -To be able to use information and communication technologies together with computer software at the basic level of European Computer Driving License required by the field.
4)	-To be able to evaluate the basic knowledge and skills acquired in the field with a critical approach, to determine and meet the learning needs. -To be able to direct his / her education to an advanced education level in the same field or to a profession at the same level. -Gaining consciousness of lifelong learning.
5)	-To have social, scientific, cultural and ethical values in the stages of gathering, applying and announcing the results related to the field. -To have sufficient consciousness about the universality of social rights, social justice, quality and cultural values, environmental protection, occupational health and safety.
6)	-To be able to conduct a basic level study independently. -To be able to take responsibility as a team member to solve unforeseen complex problems encountered in the applications related to the field. -To be able to carry out activities for the development of employees working under their responsibility within the framework of a project.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Presentation	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	Workload
Course Hours	16	48
Study Hours Out of Class	16	53
Presentations / Seminar	5	10
Final	1	2
Total Workload		113