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Machine Learning Methods for Embedded Systems

Type of study Doctoral
Language of instruction English
Code 450-6023/02
Abbreviation MSUVS
Course title Machine Learning Methods for Embedded Systems
Credits 10
Coordinating department Department of Cybernetics and Biomedical Engineering
Course coordinator prof. Ing. Michal Prauzek, Ph.D.

Subject syllabus

The course deals with areas of applications in embedded systems with implemented methods:

A) Unsupervised learning (Cluster analysis …)
B) Supervised learning (Support vector machines, regression linearization, decision trees, k-nearest neighbor algorithm, neural networks …)
C) Semi-supervised learning - methods of reinforcement learning (Markov's decision-making process, Q-learning …)

In terms of progressive optimization of the function and design of embedded systems, the cource inludes bio-inspired methods from a family of evolutionary algorithms (genetic algorithms, differential evolution, etc.) or other approaches such as particle swarm optimization.

The application areas of the course are focused to deployment in the field of Internet-of-Things devices, ultra-low power devices with energy harvesting from various domain such as sensor systems, applications associated with the concept of Industry 4.0, SmartCities and SmartMetering.

E-learning

Materials are available at https://lms.vsb.cz/?lang=en

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.