Embedded Electronic Systems for AI and ML (2026)

Machine Learning and Artificial Intelligence methods and solutions are being applied to every human activity, from agriculture to zoology. Effective implementation of ML and AI algorithms, in particular in the embedded context, is essential to enable this transition on a broad scale.
The course begins by introducing the main concepts and algorithms behind the most common Machine Learning and Artificial Intelligence methods and algorithms. Then it describes the main hardware platforms used for both training and inferencing, and their impact on the algorithm design and optimization, with a particular focus on embedded platforms and hardware acceleration support. Finally it provides hands-on experience of programming some significant applications on commonly used embedded AI platforms (e.g. Movidius, Zynq, ...).
It does not require any background on AI or ML, but a solid knowledge of linear algebra, of programming in C/C++ and some knowledge of processor architecture and hardware design.

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