Learning Outcomes:

1. Analyze case studies where AI has been integrated in embedded systems.

2. Explain the enhancement of system functionality through AI implementations.

3. Critically evaluate real-world examples to solidify conceptual understanding.

Learning Targets:

1. Deploy simple machine learning models using frameworks such as TensorFlow Lite for Microcontrollers, providing detailed examples of implementation (e.g., basic object detection) and design best practices.

2. Program and configure the Arduino IDE effectively for the integration of AI algorithms, following step-by-step tutorials and incorporating best practices for code organization and debugging.

3. Test and validate machine learning implementations using sensor data inputs, including the use of serial monitors and debugging tools to document performance and adhere to best practices.

Modules

Learning Targets:

1. Develop schematic diagrams and wiring plans that detail the integration of sensors, microcontrollers, and AI modules, using standard design tools and industry best practices (e.g., adhering to hardware layout guidelines).

2. Produce detailed system design documents that clearly outline hardware-software interactions, including annotated examples and commentary on design decisions to support best practices.

3. Incorporate safety, scalability, and performance optimization measures into system designs by referencing industry standards and including real-world benchmark examples.

Modules

Learning Targets:

1. Conduct rigorous performance analyses using debugging and monitoring tools (e.g., Arduino Serial Monitor, oscilloscopes) to track computational efficiency, providing example scenarios and best practices in troubleshooting.

2. Optimize resource usage and model accuracy through iterative testing, parameter tuning, and documenting performance improvements with clear before-and-after examples.

3. Benchmark and validate system performance against industry standards, compiling reports that include performance metrics and recommended best practices.

Modules

Learning Targets:

1. Diagnose hardware-software integration issues by applying systematic debugging techniques, utilizing real-world examples and standard diagnostic tools as best practices.

2. Implement corrective measures to resolve configuration errors and sensor misalignments, documenting each step with detailed examples and adhering to recommended practices.

3. Develop comprehensive troubleshooting documentation that includes common failure modes, corrective actions, and best practices for ongoing maintenance in AI-enabled Arduino systems.

Modules

Learning Targets:

1. Configure and utilize the Arduino IDE for effective code development and hardware management, including examples of advanced IDE settings and recommended plugins.

2. Implement and test machine learning models using frameworks like TensorFlow Lite for Microcontrollers, providing detailed project examples that illustrate best practices in model deployment on embedded systems.

3. Leverage open-source libraries, community resources, and debugging tools (e.g., Serial Plotter, logic analyzers) to boost development productivity, accompanied by best practice guidelines.

4. Integrate sensor data collection and analysis techniques, using example projects to demonstrate data-driven AI model improvements and adhering to industry-standard best practices.

Modules