EduXchange.EU

Machine Learning for Embedded Systems

IAS0360
Computer Science and ICT, Data, AI

About this course

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 autumn semester 2024/2025 which starts on 2nd of September and ends 26th of January (you can find that information under Start date section). The real course start and end dates will be announced at the beginning of September at the latest.

Learning outcomes

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.

Examination

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

Course requirements

Knowledge of Phyton and C type languaguage.

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

Resources

  • 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.

Activities

lectures, practices

Additional information

  • Credits
    ECTS 6
  • Level
    Master
  • Contact hours per week
    4
  • Instructors
    Uljana Reinsalu
  • Mode of instruction
    Online - time-independent
If anything remains unclear, please check the FAQ of TalTech (Estonia).

Offering(s)

  • Start date

    2 September 2024

    • Ends
      26 January 2025
    • Term *
      Fall semester 2024
    • Instruction language
      English
    Course is currently running
These offerings are valid for students of TU/e (The Netherlands)