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.
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.