Tiny Machine Learning (TinyML) course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course introduces the fundamentals of Tiny Machine Learning (TinyML), focusing on deploying machine learning models on low-power embedded devices. Learners will gain hands-on experience in signal processing, model optimization, and deployment on microcontrollers. The program is structured into six modules, including a final capstone project, with a total time commitment of approximately 16–24 weeks. Each module combines theory with practical labs using real hardware and sensor data.
Module 1: Foundations of TinyML
Estimated time: 40 hours
- Understand embedded systems basics
- Learn fundamentals of neural networks
- Explore constraints in memory and processing power
- Study signal processing for sensor data
Module 2: Model Training and Optimization
Estimated time: 40 hours
- Train machine learning models for embedded use
- Apply quantization techniques
- Implement model compression methods
- Evaluate latency and energy efficiency
Module 3: Deployment on Microcontrollers
Estimated time: 40 hours
- Deploy trained models to hardware devices
- Integrate sensors and data pipelines
- Debug embedded ML applications
- Measure inference performance and reliability
Module 4: Signal Processing for Embedded AI
Estimated time: 30 hours
- Acquire and preprocess sensor data
- Design feature extraction pipelines
- Optimize data flow for real-time inference
Module 5: Performance Trade-offs in Constrained Environments
Estimated time: 30 hours
- Analyze memory and power limitations
- Test models under real-time constraints
- Balance accuracy, speed, and efficiency
Module 6: Final Project
Estimated time: 50 hours
- Build an end-to-end TinyML system
- Optimize deployment for scale
- Demonstrate real-time embedded inference
Prerequisites
- Familiarity with programming (Python preferred)
- Basic understanding of machine learning concepts
- Experience with embedded systems or microcontrollers is helpful but not required
What You'll Be Able to Do After
- Deploy machine learning models on microcontrollers
- Optimize neural networks for low-power devices
- Process and analyze sensor data in real time
- Design efficient edge AI applications
- Build and present a working embedded AI solution