Applied Tiny Machine Learning (TinyML) for Scale course Syllabus

Overview: This professional certificate program is designed to equip engineers and technical learners with the skills to deploy scalable machine learning models on low-power embedded devices. The curriculum spans approximately 16–24 weeks of part-time study, with a strong focus on hands-on implementation, optimization, and system integration. Learners will progress from foundational concepts to a capstone project, gaining practical experience in TinyML deployment across real-world edge computing scenarios.

Module 1: Foundations of TinyML

Estimated time: 15 hours

• Introduction to embedded systems and microcontrollers
• Basics of machine learning inference on edge devices
• Understanding power, memory, and latency constraints
• Fundamentals of signal processing for sensor data

Module 2: Model Optimization Techniques

Estimated time: 16 hours

• Principles of model quantization and compression
• Reducing model size for memory-constrained devices
• Latency optimization for real-time inference
• Trade-offs in accuracy versus efficiency

Module 3: Deployment on Microcontrollers

Estimated time: 18 hours

• Setting up embedded development environments
• Deploying ML models using TensorFlow Lite for Microcontrollers
• Memory management and inference execution
• Evaluating energy consumption during operation

Module 4: Edge AI Systems Integration

Estimated time: 17 hours

• Integrating sensors with microcontrollers
• Designing end-to-end TinyML pipelines
• Hardware-software co-design principles
• Debugging and testing embedded ML systems

Module 5: IoT and Scalable Edge Applications

Estimated time: 16 hours

• Exploring IoT platforms for TinyML deployment
• Use cases in smart devices, robotics, and industrial automation
• Scaling TinyML solutions across device fleets
• Privacy and efficiency benefits of on-device AI

Module 6: Final Project

Estimated time: 20 hours

• Design and implement a complete TinyML application
• Optimize model performance under hardware constraints
• Demonstrate real-time inference and present results

Prerequisites

• Familiarity with Python programming
• Basic understanding of machine learning concepts
• Experience with embedded systems or microcontrollers preferred

What You'll Be Able to Do After

• Deploy machine learning models on microcontrollers
• Optimize models for memory, latency, and power efficiency
• Design and implement end-to-end TinyML systems
• Integrate sensors and embedded hardware for edge AI
• Build scalable, real-time intelligent IoT applications