COMPUTER ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Ders Genel Tanıtım Bilgileri

Course Code:

1410002007

Ders İsmi:

Introduction to Machine Learning

Ders Yarıyılı:

Spring

Ders Kredileri:

Theoretical	Practical	Credit	ECTS
3	0	3	5

Language of instruction:

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:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi Recep DURANAY

Course Lecturer(s):

Prof. Dr. Haluk GÜMÜŞKAYA

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:	The course aims to provide students with information about the basic methods and approaches in the field of machine learning and to help students gain the ability to apply machine learning methods to practical problems.
Course Content:	Basic concepts and approaches of machine learning. Guided machine learning methods. Concept learning and learning with decision trees. Bayes' theorem-based approaches in machine learning. Evolutionary approach and genetic programming. Learning and reinforcement learning with artificial neural networks, support vectors. Undirected learning methods and classification.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) 1) Knowledge of basic methods in the field of machine learning.
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
1) 2) Ability to model and solve practical problems using machine learning methods
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Introduction to Machine Learning	Course Book
2)	Concept Learning	Course Book
3)	Learning with Decision Trees	Course Book
4)	Genetic Algorithms and Genetic Programming	Course Book
5)	Project Proposal Submissions	Course Book
6)	Learning with the Bayesian Approach	Course Book
7)	Bayesian Belief Networks	Course Book
8)	Midterm
9)	Artificial neural networks	Course Book
10)	Feedback Neural Networks	Course Book
11)	Learning with Support Vectors	Course Book
12)	Learning with Reinforcement	Course Book
13)	Undirected Learning	Course Book
14)	Project Presentations	Course Book
15)	Project Presentations	Course Book
16)	Final

Sources

Course Notes / Textbooks:	Machine Learning, McGraw-Hill, T. Mitchell (1997)
References:	Machine Learning, McGraw-Hill, T. Mitchell (1997)

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2
Program Outcomes
1) PO 1.1) Sufficient knowledge in mathematics, science and computer engineering
2) PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems.
3) PO 2.1) Identifying complex engineering problems
4) PO 2.2) Defining complex engineering problems
5) PO 2.3) Formulating complex engineering problems
6) PO 2.4) Ability to solve complex engineering problems
7) PO 2.5) Ability to choose and apply appropriate analysis and modeling methods
8) PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions.
9) PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product
10) PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
11) PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
12) PO 4.3) Ability to use information technologies effectively.
13) PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments
14) PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation
15) PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection
16) PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics
17) PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics

Ders - Öğrenme Kazanımı İlişkisi

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

	Program Outcomes	Level of Contribution
1)	PO 1.1) Sufficient knowledge in mathematics, science and computer engineering
2)	PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems.
3)	PO 2.1) Identifying complex engineering problems
4)	PO 2.2) Defining complex engineering problems
5)	PO 2.3) Formulating complex engineering problems	5
6)	PO 2.4) Ability to solve complex engineering problems
7)	PO 2.5) Ability to choose and apply appropriate analysis and modeling methods
8)	PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions.
9)	PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product
10)	PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
11)	PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
12)	PO 4.3) Ability to use information technologies effectively.
13)	PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments
14)	PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation
15)	PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection
16)	PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics
17)	PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics

Öğrenme Etkinliği ve Öğretme Yöntemleri

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Homework

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Attendance	10	% 10
Presentation	1	% 20
Midterms	1	% 30
Semester Final Exam	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	35	0	0
Midterms	1	2	2
Final	1	3	3
Total Workload			47