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Embedded and Distributed AI 7,5 Credits

Course Contents

The course includes the following elements:
- Introduction to Embedded and Distributed AI (architectures, platforms, sensors)
- Introduction to Image processing and computer vision
- Feature engineering and object detection on embedded systems
- Semantic segmentation and real-time processing on embedded systems
- TinyML and applications
- Real-time tracking, 3D reconstruction and SLAM on edge devices
- Transfer learning and mobile applications (using TensorFlowJS and TensorFlowLite)
- IoT applications and using clouds for distributed system development
- Introduction to natural language processing (NLP) and examples by using Google Cloud
- Introduction to cloud computing
- Introduction to CUDA parallel programming
- Introduction to Sensor Fusion
- Introduction to Distributed/Federated Learning

Prerequisites

Passed courses at least 90 credits within the major subject Computer Engineering, Electrical Engineering (with relevant courses in Computer Engineering), or equivalent, or passed courses at least 150 credits from the programme Computer Science and Engineering, and completed course Machine Learning, 7,5 credits or equivalent. Proof of English proficiency is required.

Level of Education: Master
Course code/Ladok code: TEDS22
The course is conducted at: School of EngineeringLast modified 2021-06-07 10:52:24