Electrical and Electronic Engineering (English) Preview
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code:

JOB124

Course Name:

Machine Learning Architecture and Applications with MATLAB

Semester:

Fall
Spring

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Departmental Elective

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:

Doç. Dr. PINAR ÇAKIR HATIR

Course Lecturer(s):

Dr. Fevzi Aytaç Durmaz

Course Assistants:

Course Objective and Content

Course Objectives:	Bu ders mühendislik öğrencilerine Matlab ile temel makine öğrenmesi uygulamaları konusunda uygulamalı beceriler ile deneyim. kazandırmayı hedeflemektedir.
Course Content:	The course will provide both theoretical and practical knowledge on machine learning, deep network design, generative artificial intelligence, and reinforcement learning through a software platform. Additionally, it will equip students with the ability to develop applications using deep learning methods.

Learning Outcomes

The students who have succeeded in this course;
1) Students will be able to develop and analyze machine and deep learning models using MATLAB.
2) Students will be able to apply explainable artificial intelligence, generative artificial intelligence, and natural language processing techniques.
3) Students will be able to develop solutions for control problems using reinforcement learning methods.

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction
2)	Introduction to Machine Learning (ML) and Statistical Methods
3)	Machine Learning Applications and AutoML 1
4)	Machine Learning Applications and AutoML 2
5)	Introduction to Deep Learning (DL) and Fundamental Architectures
6)	Deep Network Design and Advanced Deep Learning Techniques 1
7)	Deep Network Design and Advanced Deep Learning Techniques 2
8)	Midterm Exam
9)	Model Interpretability and Explainable Artificial Intelligence (XAI)
10)	Generative Artificial Intelligence and Natural Language Processing (NLP)
11)	Introduction to Reinforcement Learning (RL)
12)	Reinforcement Learning and System Identification
13)	General Topic Review
14)	Project Presentations and General Evaluation

Sources

Course Notes / Textbooks:	Ders notları, videolar - Lecture notes, videos
References:	Ders notları, videolar - Lecture notes, videos

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3
Program Outcomes
1) Has sufficient knowledge in mathematics and natural sciences.
2) Has sufficient knowledge in Electrical and Electronics engineering–specific subjects.
3) Has the ability to apply theoretical and practical knowledge of mathematics, natural sciences, and Electrical and Electronics engineering to solve complex engineering problems.
4) Has the ability to identify, formulate, and solve complex engineering problems, and to select and apply appropriate analysis and modeling methods for this purpose.
5) Has the ability to design complex systems, processes, devices, or products under realistic constraints and conditions to meet specific requirements, and to apply modern design methods for this purpose.
6) Has the ability to select and use modern techniques and tools required for the analysis and solution of complex engineering problems encountered in engineering practice, and to use information technologies effectively.
7) Has the ability to design and conduct experiments, collect data, analyze and interpret results for the investigation of complex engineering problems or Electrical and Electronics engineering–specific research topics.
8) Has the ability to work effectively in disciplinary teams.
9) Has the ability to work effectively in multidisciplinary teams.
10) Has the ability to work individually.
11) Has the ability to communicate effectively in oral and written form; has knowledge of at least one foreign language; writes effective reports, understands written reports, prepares design and production reports, makes effective presentations, and gives and receives clear and understandable instructions.
12) Has awareness of the necessity for lifelong learning; accesses information, follows developments in science and technology, and continuously renews oneself.
13) Acts in accordance with ethical principles; has knowledge of professional and ethical responsibilities and of the standards used in engineering practices.
14) Has knowledge of business practices such as project management, risk management, and change management.
15) Has awareness of entrepreneurship and innovation.
16) Has knowledge of sustainable development.
17) Has knowledge of the impacts of engineering practices on health, environment, and safety on a universal and societal scale, and awareness of contemporary issues reflected in the field of engineering.
18) Has awareness of the legal consequences of engineering solutions.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Has sufficient knowledge in mathematics and natural sciences.
2)	Has sufficient knowledge in Electrical and Electronics engineering–specific subjects.
3)	Has the ability to apply theoretical and practical knowledge of mathematics, natural sciences, and Electrical and Electronics engineering to solve complex engineering problems.
4)	Has the ability to identify, formulate, and solve complex engineering problems, and to select and apply appropriate analysis and modeling methods for this purpose.
5)	Has the ability to design complex systems, processes, devices, or products under realistic constraints and conditions to meet specific requirements, and to apply modern design methods for this purpose.
6)	Has the ability to select and use modern techniques and tools required for the analysis and solution of complex engineering problems encountered in engineering practice, and to use information technologies effectively.
7)	Has the ability to design and conduct experiments, collect data, analyze and interpret results for the investigation of complex engineering problems or Electrical and Electronics engineering–specific research topics.
8)	Has the ability to work effectively in disciplinary teams.
9)	Has the ability to work effectively in multidisciplinary teams.
10)	Has the ability to work individually.
11)	Has the ability to communicate effectively in oral and written form; has knowledge of at least one foreign language; writes effective reports, understands written reports, prepares design and production reports, makes effective presentations, and gives and receives clear and understandable instructions.
12)	Has awareness of the necessity for lifelong learning; accesses information, follows developments in science and technology, and continuously renews oneself.
13)	Acts in accordance with ethical principles; has knowledge of professional and ethical responsibilities and of the standards used in engineering practices.
14)	Has knowledge of business practices such as project management, risk management, and change management.
15)	Has awareness of entrepreneurship and innovation.
16)	Has knowledge of sustainable development.
17)	Has knowledge of the impacts of engineering practices on health, environment, and safety on a universal and societal scale, and awareness of contemporary issues reflected in the field of engineering.
18)	Has awareness of the legal consequences of engineering solutions.

Assessment & Grading

Değerlendirme Yöntemleri ve Kriterleri	Number of Activities	Level of Contribution
Laboratory	4	% 20
Homework Assignments	4	% 20
Project	1	% 20
Final	1	% 40
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Preparation for the Activity	Aktivitede Harcanan Süre	Completing the Activity Requirements	Workload
Course Hours	14	0	3		42
Laboratory	4	0	2		8
Study Hours Out of Class	14	0	2		28
Project	1	0	10		10
Homework Assignments	4	0	3		12
Total Workload					100