Foundations of Embedded AI Systems provides an in-depth exploration of the integration of artificial intelligence within embedded systems. Participants will engage with core concepts, tools, and methodologies essential for developing intelligent applications that operate in constrained environments. This course emphasizes hands-on project work, enabling learners to apply theoretical knowledge to practical scenarios while fostering collaboration and innovation.
Throughout the program, participants will delve into various components of embedded AI systems, including hardware considerations, software frameworks, and machine learning algorithms tailored for embedded applications. By the end of the course, learners will have the opportunity to publish their project results in Cademix Magazine, enhancing their professional visibility and contributing to the academic community.
Overview of Embedded Systems and AI Integration
Fundamentals of Microcontrollers and Processors
Programming Languages for Embedded AI (C, Python)
Machine Learning Algorithms for Embedded Devices
Sensor Integration and Data Acquisition Techniques
Real-Time Operating Systems (RTOS) for AI Applications
Development Tools and Software for Embedded AI
Performance Optimization Techniques in Embedded AI
Case Studies of Embedded AI in Industry (e.g., IoT, Robotics)
Final Project: Design and Implement an Embedded AI System