PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
MCTE 522	MICROCONTROLLER-BASED ARTIFICIAL INTELLIGENCE APPLICATIONS	3 + 0	1st Semester	7,5

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	The aim of this course is to teach students the fundamentals of microcontroller-based artificial intelligence applications and to provide them with the skills to apply this technology in different areas.
Course Content	1. Microcontroller systems and architectures. 2. Microcontroller programming languages and environments. 3. Artificial intelligence technologies and basic principles. 4. Artificial neural networks and deep learning techniques. 5. Design and development of microcontroller based artificial intelligence applications. 6. Application examples: robotics, industrial automation, internet of things (IoT) etc. 7. Performance analysis and improvement of microcontroller based artificial intelligence applications.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.

COURSE LEARNING OUTCOMES

1	Learn microcontroller programming languages and environments
2	Understand the fundamentals of artificial neural networks.
3	Understand and apply deep learning methods
4	Understand and apply sensor usage and data acquisition methods
5	Create and use classification models effectively
6	Understand and apply object perception and tracking methods
7	Learn artificial intelligence applications in robotics and industrial automationLearn artificial intelligence applications in robotics and industrial automation
8	Gain project management skills and prepare project presentations.

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11	PO 12
LO 001	5	5	5	4	4	3	3	5	4	5	4	3
LO 002	5	5	5	4	4	3	3	5	4	5	4	3
LO 003	5	5	5	4	4	3	3	5	4	5	4	3
LO 004	5	5	5	4	4	3	3	5	4	5	4	3
LO 005	5	5	5	4	4	3	3	5	4	5	4	3
LO 006	5	5	5	4	4	3	3	5	4	5	4	3
LO 007	5	5	5	4	4	3	3	5	4	5	4	3
LO 008	5	5	5	4	4	3	3	5	4	5	4	3
Sub Total	40	40	40	32	32	24	24	40	32	40	32	24
Contribution	5	5	5	4	4	3	3	5	4	5	4	3

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Mid-terms	1	75	75
Final examination	1	78	78
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2024-2025 Spring	1	ARMAĞAN BOZKURT
Details	2023-2024 Spring	1	ARMAĞAN BOZKURT

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester	Mode of Delivery
MCTE 522	MICROCONTROLLER-BASED ARTIFICIAL INTELLIGENCE APPLICATIONS	3 + 0	1	Turkish	2024-2025 Spring	Face to Face

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Asts. Prof. Dr. ARMAĞAN BOZKURT	armbozkurt@pau.edu.tr		TEK A0209	%70

Goals		The aim of this course is to teach students the fundamentals of microcontroller-based artificial intelligence applications and to provide them with the skills to apply this technology in different areas.
Content		1. Microcontroller systems and architectures. 2. Microcontroller programming languages and environments. 3. Artificial intelligence technologies and basic principles. 4. Artificial neural networks and deep learning techniques. 5. Design and development of microcontroller based artificial intelligence applications. 6. Application examples: robotics, industrial automation, internet of things (IoT) etc. 7. Performance analysis and improvement of microcontroller based artificial intelligence applications.

Topics

Weeks	Topics
1	Fundamentals of Microcontrollers
2	Programming Languages
3	Input/Output Operations
4	Artificial Neural Networks
5	Deep Learning
6	Linear Regression
7	Classification Algorithms
8	Data Processing and Collection Techniques
9	Data Analysis and Management
10	Internet of Things (IoT) Applications
11	Sensors and Data Flow
12	Practical Application Skills
13	Database Management
14	Application and Project Development

Materials

Materials are not specified.

Resources

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	50	Final Exam
Midterm Exam	50	Midterm Exam

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student