Machine Learning for Embedded Systems

Overview of main algorithms used in machine learning. Basics of model creation. Building of training data sets and using them. Main concepts of model optimization and their use for embedded systems. No special hardware is developed. Individual tasks and project assignments (by teams of 2-3 members) are solved during the course.

NB! This course will take place in spring semester 2024/2025 which starts on 3rd of February and ends on 16th of June (you can find that information under Start date section). The real course start and end dates will be announced at the beginning of February at the latest.

After successfully completing the course, the student:

• has an overview of various machine learning models suitable for embedded systems;
• chooses and uses appropriate machine learning model to solve a specific task;
• chooses appropriate hardware to implement machine learning model;
• uses tools to train and build models;
• creates and uses training data correctly;
• optimizes models depending on hardware limitations.

Final assessment can consist of one test/assignment or several smaller assignments completed during the whole course. After declaring a course the student can re-sit the exam/assessment once. Assessment can be graded or non-graded. For specific information about the assessment process please get in touch with the contact person of this course. For specific information about grade transfer please contact your home university

Knowledge of Phyton and C type languaguage.

NB! Students are ready to purchase equipment/components required for the labs themselves (up to 100 euros).

• TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers, Pete Warden, D. Situnayake.
• Embedded Deep Learning, B. Moons, D. Bankman, M. Verhelst.
• Embedded Vision: An Introduction, S. R. Vijayalakshmi, S. Muruganand.

lectures, practices