The aim of the course Machine learning methods for embedded systems is to introduce students current modern approaches in the field of machine learning methods that are directly implementable in computationally limited embedded systems. Students are also introduced to the possibilities of optimizing the development or function of embedded systems using machine learning methods. Machine learning methods are being used today in many application areas of embedded systems and microcontrollers, and this situation will continue in the future. In this course, students will learn practical applications and application of these modern approaches in real implementation.
Literature
[1] Burkov, Andriy. Machine Learning Engineering. 2020.
[2] Burkov, Andriy. The Hundred-Page Machine Learning Book. 2019.
[3] Mitchell, Tom M. Machine Learning. McGraw Hill, 2017.
[4] Harrington, Peter. Machine learning in action. Manning Publications Co, 2012.
Advised literature
[1] Embedded deep learning: algorithms, architectures and circuits for always-on neural network processing. Springer Science+Business Media, 2018.
[2] Reinforcement learning. Springer Science+Business Media, 2017.
[3] Eiben, A. E., a J. E. Smith. Introduction to Evolutionary Computing. Springer Verlag, 2015.
[4] Aggarwal, Charu C., a Chandan K. Reddy, editoři. Data clustering: algorithms and applications. Chapman and Hall/CRC, 2014.