Federal University of Rio Grande do Norte

Technology Center

:books: Daniel Situnayake and Pete Warden. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers. [Link]
:books: Gian Marco Iodice. TinyML Cookbook: Combine Artificial Intelligence and Ultra-low-power Embedded Devices to Make the World Smarter [Link]
:books: Aurélien Géron. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow [Link]
:books: François Chollet. Deep Learning with Python [Link]

Week 01: Course Outline

Machine Learning Fundamentals
- You'll learn how machine learning models work, how to build them, and how to optimize them. By the end, you’ll know the basics behind building models that will make data-driven predictions.
- :hourglass_flowing_sand: Estimated time: 10h
Git and Version Control
- You'll learn how to: a) organize your code using version control, b) resolve conflicts in version control, c) employ Git and Github to collaborate with others.
- :facepunch: getting a git repository.
- :hourglass_flowing_sand: Estimated time: 5h
Complementary materials
- Google Colab Introduction
- Google Colab Cont. [optional]
- :hourglass_flowing_sand: Estimated time: 2h

Week 02: TinyML Fundamentals

Why our business need AI? And bigger is not always better!!
How do we enable TinyML?
- Three fundamental steps to explore a TinyML solution
  - Input
  - Processing
  - Output and final remarks
  - :hourglass_flowing_sand: Estimated time: 30min to 1h.
- :page_facing_up: Further reading paper
  - Vijay Janapa Reddi et al. Widening Access to Applied Machine Learning with TinyML
  - :hourglass_flowing_sand: Estimated time: 4h
Machine Learning Fundamentals
- What is Machine Learning (ML)?
- ML types
- Main challenges of ML
  - Variables, pipeline, and controlling chaos
  - Train, dev and test sets
  - Bias vs Variance
- :hourglass_flowing_sand: Estimated time: 2h
Calculus For Machine Learning
- You'll learn how to: a) define mathematical functions using calculus; b) employ intermediate machine learning techniques.
- :hourglass_flowing_sand: Estimated time: 6h

Week 03: TinyML Challenges

What are the challenges for TinyML?
AI lifecycle and ML workflow
- AI lifecycle introduction
- AI infrastructure
- A typical ML workflow
- A TinyML workflow
- :hourglass_flowing_sand: Estimated time: 30min
ML evaluation metrics
- How to choose an evaluation metric?
- Threshold metrics
- Ranking metrics
- :hourglass_flowing_sand: Estimated time: 1h
Linear Algebra For Machine Learning
- You'll learn how to: a) Understand the key ideas to understand linear systems; b) Apply the concepts to machine learning techniques.
- :hourglass_flowing_sand: Estimated time: 6h
:page_facing_up: Further reading paper
- Visal Rajapakse et al. Intelligence at the Extreme Edge: A Survey on Reformable TinyML
- Sam Leroux et al. TinyMLOps: Operational Challenges for Widespread Edge AI Adoption
- :hourglass_flowing_sand: Estimated time: 10h

Week 04: Deep Learning Fundamentals I