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Embedded and Distributed AI 7,5 Credits

Course Contents

The course aims to create an overall understanding of knowledge representation and processing in AI, covering the span from the semantic web through distributed systems all the way to deep learning and edge computing.

The course covers the following topics:
- Semantics, Ontologies, and Knowledge Graphs
- Distributed Sensors
- Edge computing
- Deep learning
- Image analysis

The course will include laboratory work with the following main themes:
- Applying semantics to sensor environments: enriching data with contextual or externally sourced information, integrating heterogenous data sources and sensors, basic inference reasoning over knowledge graphs
- Data gathering with a distributed sensor network, implemented using Raspberry Pi/C++
- Image analysis using deep learning, implemented using GPGPU with CUDA/C++

Prerequisites

Passed courses at least 90 credits within the major subject Product Development, and completed course Machine Learning, 7,5 credits or equivalent. Proof of English proficiency is required.

Level of Education: Master
Course code/Ladok code: TEDS20
The course is conducted at: School of EngineeringLast modified 2019-12-02 10:53:19

Content updated 2020-08-25