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MF2143 Introduction to Embedded Machine Learning 7.5 credits

Embedded machine learning is a rapidly growing field at the intersection of artificial intelligence and low-power embedded systems. This technology is crucial for many mechatronics products, where integrating machine learning algorithms into resource-constrained embedded and edge devices plays a pivotal role in their success. Unlike traditional machine learning, which relies on powerful cloud-based resources, embedded machine learning enables on-device data processing in mechatronics products, offering increased privacy, reduced latency, and lower power consumption.

The market for products with embedded machine learning, spanning industries such as industrial automation, healthcare, IoT, and smart home solutions, is projected to grow from 15 million units in 2020 to 2.5 billion by 2030, with a compound annual growth rate of 167%.

This course bridges the gap between conventional machine learning techniques and embedded systems, equipping the course participants with basic knowledge and skills that that are highly relevant to the development of smart mechatronics products.

 

Information per course offering

Termin

Information for Autumn 2025 Start 27 Oct 2025 programme students

Course location

KTH Campus

Duration
27 Oct 2025 - 12 Jan 2026
Periods

Autumn 2025: P2 (7.5 hp)

Pace of study

50%

Application code

50758

Form of study

Normal Daytime

Language of instruction

English

Number of places

Places are not limited

Target group
Conditionally elective for TMEKM Open for all students as long as the course can be included in the programme. The course is suitable for incoming exchange students. The course is offered in agreement with the course leader
Planned modular schedule
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Part of programme
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Contact

Examiner
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Course coordinator
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Teachers
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Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus MF2143 (Autumn 2025–)
Headings with content from the Course syllabus MF2143 (Autumn 2025–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

This course provides an introduction to the application of machine learning in resource-constrained environments. The course includes lectures, seminars and laboratory sessions. Students learn to train, deploy and evaluate machine learning models on microcontrollers. Key topics include:

- Overview of machine learning and deep learning.

- Overview of embedded systems.

- TinyML for embedded machine learning: concepts, development environments and applications.

- Final project: Students implement and demonstrate a TinyML application.

Intended learning outcomes

After passing the course, the student should be able to:

1. Clarify the basic principles of machine learning and their implementation with embedded systems, with the aim of understanding how machine learning can be integrated into mechatronic products.

2. Understand the limitations and challenges of implementing machine learning on microcontrollers, in order to assess the suitability of the technology for specific mechatronic products.

3. Be able to use modern integrated development environments to train basic machine learning models and implement them on microcontrollers, in order to be able to apply the latest technologies in the development of mechatronic products.

4. Be able to evaluate the performance of embedded machine learning models, in order to ensure the satisfactory implementation of the technology.

Literature and preparations

Specific prerequisites

At least 3 credits in basic programming skills (Python and C preferred).

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

Examination and completion

Grading scale

A, B, C, D, E, FX, F

Examination

  • PRO1 - Project, 4.5 credits, grading scale: A, B, C, D, E, FX, F
  • LAB1 - Laboration assignment, 2.0 credits, grading scale: P, F
  • SEM1 - Seminar assignment, 1.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

If the course is discontinued, students may request to be examined during the following two academic years.

Other requirements for final grade

- Attendance and participation in laboratory sessions and seminars are compulsory.

Examiner

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Mechanical Engineering

Education cycle

Second cycle