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Course Outline
Introduction to TinyML
- What is TinyML?
- The importance of machine learning on microcontrollers
- Comparing traditional AI with TinyML
- Overview of required hardware and software
Establishing the TinyML Environment
- Installing Arduino IDE and configuring the development setup
- Introduction to TensorFlow Lite and Edge Impulse
- Flashing and configuring microcontrollers for TinyML tasks
Constructing and Deploying TinyML Models
- Understanding the TinyML workflow
- Training a basic machine learning model for microcontrollers
- Converting AI models into TensorFlow Lite format
- Deploying models onto hardware devices
Optimizing TinyML for Edge Devices
- Decreasing memory and computational footprint
- Techniques for quantization and model compression
- Benchmarking TinyML model performance
TinyML Applications and Use Cases
- Gesture recognition using accelerometer data
- Audio classification and keyword spotting
- Anomaly detection for predictive maintenance
TinyML Challenges and Future Trends
- Hardware limitations and optimization strategies
- Security and privacy concerns in TinyML
- Future advancements and research in TinyML
Summary and Next Steps
Requirements
- Foundational programming skills (in Python or C/C++)
- Familiarity with machine learning principles (recommended but not mandatory)
- Knowledge of embedded systems (optional but beneficial)
Target Audience
- Engineers
- Data scientists
- AI enthusiasts
14 Hours
