BİLGİSAYAR MÜHENDİSLİĞİ (YL) (TEZLİ) (İNGİLİZCE)
Qualification Awarded	Program Süresi	Toplam Kredi (AKTS)	Öğretim Şekli	Yeterliliğin Düzeyi ve Öğrenme Alanı
Master's ( Second Cycle) Degree	2	120	FULL TIME	TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 7 QF-EHEA:Second Cycle TR-NQF-HE, ISCED (1997-2013): 48,52

Ders Genel Tanıtım Bilgileri

Course Code:

3017002040

Ders İsmi:

Machine Learning

Ders Yarıyılı:

Spring

Ders Kredileri:

Theoretical	Practical	Labs	Credit	ECTS
3	0	0	3	6

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Other Recommended Topics for the Course:

Type of course:

Department Elective

Course Level:

Master

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

QF-EHEA:Second Cycle

EQF-LLL:7. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi Recep DURANAY

Course Lecturer(s):

Course Assistants:

Course Objectives:	Bu, Makine Öğrenmesi alanında lisansüstü düzeyde bir giriş dersidir. Makine Öğrenmesinde birçok kavram ve algoritmaya, teoriye ve pratik çalışmaya geniş bir giriş yapar. Öğreticiyle öğrenme, öğreticisiz öğrenme ve pekiştirmeli öğrenmede temel kavramlar ve seçilen makine öğrenmesi algoritmaları sunulmaktadır.
Course Content:	Regression, Basic Concepts of Machine Learning and Logistic Regression, Classification and Model Evaluation, Neural Networks, Applying Machine Learning, Support Vector Machines, Decision Trees, Ensemble Learning and Random Forests, Unsupervised Learning: Clustering, Anomaly Detection, and Dimensionality Reduction, Recommender Systems, Large Scale Machine Learning, Reinforcement Learning

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Knowledge about the basic methodologies in machine learning.
2 - Skills
Cognitive - Practical
1) Ability to use knowledge to formulate, and solve practical problems using machine learning techniques.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Week	Subject	Related Preparation
1)	Derse Genel Bakış, Makine Öğrenmesine Giriş, Makine Öğrenimi Uygulamaları
2)	Tek Değişkenli Regresyon, Çoklu Girdi Değişkenli Regresyon, Düzenleme, Regresyon Örnekleri
3)	Makine Öğreniminin Temel Kavramları ve Düzenli Doğrusal Modeller
4)	Classification and Logistic Regression, Model Evaluation
5)	Neural Networks-Representation
6)	Neural Networks-Training
7)	Applying Machine Learning and Large Scale Machine Learning
8)	Midterm Exam
9)	Support Vector Machines
10)	Decision Trees, Ensemble Learning and Random Forests
11)	Unsupervised Learning-Clustering, Anomaly Detection and Dimensionality Reduction
12)	Recommender Systems
13)	Reinforcement Learning
14)	Project Demonstration / Midterm Exam II

Course Notes / Textbooks:	No textbook. But the following and similar other books will be used for machine learning practice: Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow (2019), 2nd Edition, Aurélien Geron Python Jupyter Notebook examples: https://github.com/ageron/handson-ml
References:	No textbook. But the following and similar other books will be used for machine learning practice: Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow (2019), 2nd Edition, Aurélien Geron Python Jupyter Notebook examples: https://github.com/ageron/handson-ml

Ders Öğrenme Kazanımları	1	2
Program Outcomes
1) Ability to reach wide and deep knowledge through scientific research in the field of Computer Science and Engineering, evaluate, interpret and apply.
2) Ability to use scientific methods to cover and apply limited or missing knowledge, and to integrate the knowledge of different disciplines.
3) Ability to construct Computer Science and Engineering problems, develop methods to solve the problems and use innovative methods in the solution.
4) Ability to develop new and/or original ideas and algorithm; develop innovative solutions in the design of system, component or process.
5) Ability to have extensive knowledge about current techniques and methods applied in Computer Engineering and their constraints.
6) Ability to design and implement analytical modeling and experimental research, solve and interpret complex situations encountered in the process.
7) Ability to use a foreign language (English) at least at the level of European Language Portfolio B2 in verbal and written communication.
8) Ability to lead in multidisciplinary teams, develop solutions to complex situations and take responsibility.
9) Awareness of the social, legal, ethical and moral values, and the ability to conduct research and implementation work within the framework of these values.
10) Awareness of the new and emerging applications in Computer Science and Engineering field, and the ability to examine them and learn if necessary.

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

	Program Outcomes	Level of Contribution
1)	Ability to reach wide and deep knowledge through scientific research in the field of Computer Science and Engineering, evaluate, interpret and apply.
2)	Ability to use scientific methods to cover and apply limited or missing knowledge, and to integrate the knowledge of different disciplines.
3)	Ability to construct Computer Science and Engineering problems, develop methods to solve the problems and use innovative methods in the solution.
4)	Ability to develop new and/or original ideas and algorithm; develop innovative solutions in the design of system, component or process.
5)	Ability to have extensive knowledge about current techniques and methods applied in Computer Engineering and their constraints.
6)	Ability to design and implement analytical modeling and experimental research, solve and interpret complex situations encountered in the process.
7)	Ability to use a foreign language (English) at least at the level of European Language Portfolio B2 in verbal and written communication.
8)	Ability to lead in multidisciplinary teams, develop solutions to complex situations and take responsibility.
9)	Awareness of the social, legal, ethical and moral values, and the ability to conduct research and implementation work within the framework of these values.
10)	Awareness of the new and emerging applications in Computer Science and Engineering field, and the ability to examine them and learn if necessary.

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Homework
Uygulama
Bireysel Proje
Grup Projesi
Sunum