Applications of TinyML

This introductory module provides an overview of Tiny Machine Learning (TinyML) applications and sets the foundation for the course. Students are introduced to the core concepts, objectives, and practical applications of TinyML in real-world scenarios.

This module explores the complete lifecycle of AI projects and the machine learning workflow specific to TinyML applications. Students learn about the stages from data collection to deployment, with emphasis on considerations for resource-constrained devices.

This module focuses on implementing machine learning models on mobile and IoT devices. Students learn about the specific challenges and requirements of deploying ML models on resource-constrained devices and edge computing platforms.

This advanced module builds upon Part 1, diving deeper into optimization techniques and advanced deployment strategies for mobile and IoT devices. Students learn about model optimization, hardware acceleration, and performance tuning.

This module covers the implementation of keyword spotting systems using TinyML. Students learn about audio processing, wake word detection, and building efficient models for voice-activated systems similar to "OK Google" and "Alexa".

This module focuses on data engineering practices specific to TinyML applications. Students learn about data collection, preprocessing, augmentation, and optimization techniques for training efficient models on embedded devices.

This module explores visual wake word systems, teaching students how to build and deploy computer vision models for tiny devices. Topics include image processing, model optimization, and practical applications in embedded vision systems.

This module covers anomaly detection techniques using TinyML. Students learn how to implement efficient anomaly detection systems on resource-constrained devices for applications such as predictive maintenance and quality control.

This module addresses ethical considerations and best practices in TinyML development. Students learn about privacy, security, bias mitigation, and responsible deployment of AI systems on edge devices.

This concluding module synthesizes the key concepts covered throughout the course. Students review the major topics and understand how different TinyML applications integrate in real-world scenarios.