Information Systems Engineering
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Ders Genel Tanıtım Bilgileri

Course Code:

FET313

Ders İsmi:

Machine Learning

Ders Yarıyılı:

Spring

Ders Kredileri:

Theoretical	Practical	Laboratory	ECTS
2	1	0	5

Language of instruction:

Turkish

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Type of course:

Bölüm Seçmeli

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator :

Asst. Prof. Dr. ARiF YELĞİ

Course Lecturer(s):

Asst. Prof. Dr. KÜBRA EROĞLU

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:	This course covers machine learning algorithms both theoretically and practically on real data. At the end of the course, students are expected to learn the basic concepts in machine learning and apply machine learning algorithms on data, establish a relationship between learning models and engineering applications, and actively participate in the courses throughout the semester.
Course Content:	First look at machine learning and basic concepts of machine learning, learning theory and its types, Bayesian learning and decision trees, artificial neural networks and genetic algorithms, unsupervised learning and reinforcement learning and ethics in machine learning.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
2 - Skills
Cognitive - Practical
1) To understand the basic concepts of machine learning.
2) To be able to solve a problem involving one of the different types of learning.
3) To be able to apply machine learning algorithms on data.
4) To be able to develop a project using machine learning approach.
5) To be able to evaluate the learning model.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Introduction to Machine Learning	Repetition of previous topics
2)	Basic Concepts in Machine Learning	Repetition of previous topics
3)	Learning Theory and Types of Learning	Repetition of previous topics
4)	Bayesian Learning	Repetition of previous topics
5)	Decision Tree Learning	Repetition of previous topics
6)	Artificial neural networks	Repetition of previous topics
7)	Multilayer Artificial Neural Networks	Repetition of previous topics
8)	MIDTERM	Repetition of previous topics
9)	Genetic Algorithms	Repetition of previous topics
10)	Example-Based Learning	Repetition of previous topics
11)	Unsupervised Learning	Repetition of previous topics
12)	Kohonen Networks	Repetition of previous topics
13)	Supportive Learning	Repetition of previous topics
14)	Privacy in Machine Learning	Repetition of previous topics
15)	Privacy in Machine Learning	Repetition of previous topics
16)	Final Exam	Repetition of previous topics

Sources

Course Notes / Textbooks:
References:	Ethem ALPAYDIN, Introduction to Machine Learning, The MIT Press, second edition, 2010. Tom Mitchell, Machine Learning, McGraw-Hill.

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	3	4	5
Program Outcomes

Ders - Öğrenme Kazanımı İlişkisi

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

Program Outcomes

Level of Contribution

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
total		%
PERCENTAGE OF SEMESTER WORK		% 0
PERCENTAGE OF FINAL WORK		%
total		%