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	2nd Semester	7,5

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	The aim of this course is to introduce students to the fundamental principles of microcontroller-based artificial intelligence applications, develop their knowledge and skills in sensor data acquisition and processing, machine learning model development and optimization, and the deployment of AI models on microcontrollers for real-time applications.
Course Content	Microcontrollers and Edge AI, STM32-based AI development environments, sensor data acquisition, fundamentals of machine learning and deep learning, artificial neural networks, TinyML, data preprocessing and feature extraction, classification and regression algorithms, TensorFlow Lite for Microcontrollers, STM32Cube.AI, IoT and Edge AI applications, model optimization, energy efficiency, and microcontroller-based artificial intelligence project development.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.

COURSE LEARNING OUTCOMES

1	Explain the fundamental components, operating principles, and application areas of microcontroller-based artificial intelligence systems.
2	Acquire sensor data, perform data preprocessing, and prepare datasets suitable for training machine learning models.
3	Develop and evaluate artificial intelligence models for microcontroller-based applications using machine learning and deep learning techniques.
4	Deploy, optimize, and execute artificial intelligence models on STM32-based microcontrollers for real-time applications.
5	Design, test, analyze, and optimize the performance of microcontroller-based artificial intelligence systems.
6	Develop innovative engineering solutions using current TinyML, Edge AI, and Internet of Things (IoT) technologies.

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	4	3	2	1	1	2	5	2	2	2	1
LO 002	4	5	4	4	2	2	2	5	2	3	3	1
LO 003	4	5	5	4	2	2	3	5	2	3	4	1
LO 004	4	5	5	4	2	2	3	5	2	4	4	1
LO 005	3	4	5	5	3	3	3	5	3	5	5	2
LO 006	3	4	5	4	3	3	5	5	3	4	5	2
Sub Total	23	27	27	23	13	13	18	30	14	21	23	8
Contribution	4	5	5	4	2	2	3	5	2	4	4	1

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 introduce students to the fundamental principles of microcontroller-based artificial intelligence applications, develop their knowledge and skills in sensor data acquisition and processing, machine learning model development and optimization, and the deployment of AI models on microcontrollers for real-time applications.
Content		Microcontrollers and Edge AI, STM32-based AI development environments, sensor data acquisition, fundamentals of machine learning and deep learning, artificial neural networks, TinyML, data preprocessing and feature extraction, classification and regression algorithms, TensorFlow Lite for Microcontrollers, STM32Cube.AI, IoT and Edge AI applications, model optimization, energy efficiency, and microcontroller-based artificial intelligence project development.

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