Tiny Machine Learning
This course provides both theoretical foundations and hands-on experience in machine learning for small, resource-constrained devices.
Students will first study the fundamentals of tiny machine learning algorithms before applying their knowledge in a collaborative group project, solving real-world challenges.
This course is structured into two primary parts:
Foundations of Tiny Machine Learning
In the initial phase, students will focus on the theoretical and practical aspects of tiny machine learning. Topics include:
- Fundamentals of tiny machine learning algorithms designed for resource-constrained devices.
- Theoretical principles underpinning algorithm design and optimization.
- Hands-on assignments to reinforce and apply these concepts in practical scenarios.
Group Project Work
The second phase centers on a collaborative group project where students will tackle a current and relevant problem in machine learning for small devices. In this component, students will:
- Leverage the knowledge and skills acquired from previous courses.
- Conduct comprehensive literature reviews to identify innovative approaches.
- Collaborate in teams to develop and implement a solution addressing real-world challenges.
This two-part approach ensures that students build a strong theoretical foundation while also gaining practical, hands-on experience through collaborative problem-solving.
At the beginning of the course, the examiner provides a detailed course description that outlines the content to be covered.
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Course structure
Teaching format
The teaching consists of lectures/lessons, laboratory work, project work, seminars, mandatory oral presentation and written report.
The language of instruction is English
Assessment
The course is examined through assignments and project work.
Examiner
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Schedule
The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course. -
Course literature
Note that the course literature can be changed up to two months before the start of the course.
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Contact
Study counsellors - master
- Visiting address
Nod Buildning, Borgarfjordsgatan 12, Kista
- Office hours
Please contact us via email if you want to book a meeting. We are available on Campus in Kista and via Zoom.
- Phone hours
Thursday 12.30–2 pm
- Irregular office hours
No phone hours on Thursday 1 May.
