Master's Programme in Machine Learning, Systems and Control
Lund University
Start date:
End date:
Pace of study: 100 %
Published education catalogue
Education information from the published source. The education record and its time-bound offerings are kept separate.
Code: TAMSR
<p>The amount of available data in the world is rapidly expanding. This programme prepares you for a flexible, future-proof career in a field where advanced algorithms are used to analyse large datasets across a wide range of applications.</p><p>In this master’s programme you get to explore how data-driven methods can be applied in a variety of fields such as self-driving cars, healthcare, energy optimisation, and manufacturing. You will learn how to combine techniques from statistical analysis, mathematics, signal processing, image analysis, and control theory to extract insights from large and complex datasets. </p><p>The programme is offered by LTH, Lunds Tekniska Högskola (The Faculty of Engineering at Lund University). It is a collaboration between the departments of Mathematics, Automatic Control, Computer Science and Electrical and Information Technology. Research and education are closely linked, and the four departments have a long tradition of successful cooperation. </p><p>Next to the faculty is Ideon Science Park, a lively environment with a long tradition of innovation in software, internet of things, telecommunication, energy and new materials.</p><p> </p><p> </p>
A Bachelor's degree in science, technology, engineering, mathematics (STEM) or equivalent. Completed courses in mathematics (linear algebra, calculus in one and several variables, transforms and linear filtering) of at least 30 credits/ECTS as well as one completed course in mathematical statistics, one in computer programming or computer science and one in control engineering. English 6.
Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.
Lund University
Start date:
End date:
Pace of study: 100 %
Retrieved: .
Published: .
Publication version: 8e217193-f5fa-4778-b085-a4521fd03e8d
Checksum: 1b0dc54c0fc8a359f83ba9dc8f9d468479ce432de4c03bce3b8b33dd67fe3f6c
Last changed according to the source: 2026-02-10T09:10:00