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:	-
References:	Ders notları, videolar - Lecture notes, videos

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3
Program Outcomes
1) Adequate knowledge in mathematics, science and Electrical and Electronics engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.
2) Ability to identify, formulate, and solve complex electrical and electronics engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.
3) Ability to design a complex circuit, device or system to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.
4) Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in electrical and electronics engineering applications; ability to use information technologies effectively.
5) Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or electrical and electronics engineering research topics.
6) Ability to work effectively within and multidisciplinary teams; individual study skills.
7) Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effectice reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9) To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in electrical and electronics engineering applications.
10) Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11) Knowledge of the effects of electrical and electronics engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in electrical and electronics engineering; awareness of the legal consequences of electrical and electronics engineering solutions.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in mathematics, science and Electrical and Electronics engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.
2)	Ability to identify, formulate, and solve complex electrical and electronics engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.
3)	Ability to design a complex circuit, device or system to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.
4)	Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in electrical and electronics engineering applications; ability to use information technologies effectively.
5)	Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or electrical and electronics engineering research topics.
6)	Ability to work effectively within and multidisciplinary teams; individual study skills.
7)	Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effectice reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9)	To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in electrical and electronics engineering applications.
10)	Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11)	Knowledge of the effects of electrical and electronics engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in electrical and electronics engineering; awareness of the legal consequences of electrical and electronics engineering solutions.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Laboratory	4	% 20
Homework Assignments	4	% 20
Project	1	% 20
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Preparation for the Activity	Spent for the Activity Itself	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