Lead Teaching Assistant for "Mobile Devices"

Key topics covered included:

• Platform Setup: Installing and configuring the Nvidia Jetson Xavier for optimal performance.
• Programming for Mobile Devices: In-depth tutorials on Python and C++ tailored for embedded applications.
• Performance Optimization: Techniques for maximizing computational efficiency and minimizing latency on mobile hardware.
• Sensor Integration and Data Processing: Practical sessions on interfacing with various sensors and handling real-time data streams.
• Deploying Machine Learning Models: Strategies for running and optimizing machine learning algorithms directly on the device.

Hands-on lab sessions were a core component of the course, allowing students to implement projects that bridged theoretical concepts with real-world applications. This experience empowered them to design and deploy efficient, scalable solutions on mobile edge devices.