Course 1: Fundamentals of TinyML
Chapter 1 · Course 1
Welcome to TinyML
Core concepts, challenges, and opportunities of TinyML. Overview of the specialization, introduction to responsible AI, and getting started with Google Colab and TensorFlow.
Textbook companion: Vol I Ch 1–2 · Hardware Kits§1.1 Course Overview
| Topic | Type |
|---|---|
| What is this specialization all about? | Slides |
| Who is this course aimed at (everyone)? | Slides |
| What will you learn? | Slides |
| How is the course structured? | Slides |
§1.2 The Future of ML is Tiny and Bright
| Topic | Type |
|---|---|
| What is (tiny) Machine Learning? | Slides |
| TinyML application case studies | Reading |
| How do we enable TinyML? | Slides |
§1.3 TinyML Challenges
| Topic | Type |
|---|---|
| What are the Challenges for TinyML (Part A)? | Slides |
| What are the Challenges for TinyML (Part B)? | Slides |
| What are the Challenges for TinyML (Part C)? | Slides |
| What are the Challenges for TinyML (Part D)? | Slides |
| Why the Future of ML is Tiny | Reading |
| Introduction to Responsible AI/ML | Slides |
| Case Studies of Responsible AI/ML Failures | Reading |
§1.4 Getting Started
| Topic | Type |
|---|---|
| What resources are needed for this course? | Slides |
| Colab in this Course | Slides |
| Learning Colab | Colab |
| Tips for using Colab | Reading |
| Sample TensorFlow code | Reading |
Chapter 2 · Course 1
Introduction to (Tiny) ML
The machine learning paradigm, deep learning building blocks, CNNs, computer vision, and responsible AI design. Hands-on with loss functions, gradient descent, neural networks, and image classification.
Textbook companion: Vol I Ch 3–6§2.1 The Machine Learning Paradigm
| Topic | Type |
|---|---|
| The machine learning paradigm | Slides |
| Thinking about Loss | Slides |
| Exploring Loss | Colab |
| Minimizing Loss | Slides |
| Exploring Gradient Descent | Colab |
| First Neural Network | Slides |
| First Neural Network in Colab | Colab |
| More on Neural Networks | Reading |
| Machine Learning Case Studies | Reading |
| Neural Network Coding Assignment | Colab |
| Assignment Solution | Reading |
§2.2 The Building Blocks of Deep Learning
| Topic | Type |
|---|---|
| Initialization in Machine Learning | Reading |
| Understanding Neurons | Slides |
| Neurons in Action | Colab |
| Coding Stepback | Reading |
| Multi-Layer Neural Network | Colab |
| Introduction to Classification | Slides |
| Coding Exercise: DNN | Colab |
| Training, Validation, and Test Data | Slides |
| Realities of Coding Neural Networks | Reading |
| Coding Assignment: DNNs | Colab |
| Assignment Solution | Reading |
§2.3 Exploring Machine Learning Scenarios
| Topic | Type |
|---|---|
| Quick Recap | Reading |
| Introducing Convolutions | Slides |
| Coding Exercise: Filters | Colab |
| From DNN to CNN | Slides |
| Coding Exercise: CNN | Colab |
| Mapping Features to Labels | Reading |
| Coding Exercise: Computer Vision | Colab |
| Coding Assignment: CNNs | Colab |
| Assignment Solution | Reading |
§2.4 Building a Computer Vision Model
| Topic | Type |
|---|---|
| Quick Recap | Reading |
| Preparing Image Data | Slides |
| Coding Exercise: Complex Images | Colab |
| TFDS for Image Data | Reading |
| Overfitting | Slides |
| Coding Exercise: Image Augmentation | Colab |
| Dropout Regularization | Reading |
| Exploring Loss Functions and Optimizers | Reading |
| Coding Assignment: Enhancing a CNN | Colab |
| Assignment Solution | Reading |
§2.5 Responsible AI Design
| Topic | Type |
|---|---|
| What Am I Building? What's the Goal? | Slides |
| Development and TinyML | Reading |
| Who Am I Building This For? | Slides |
| What Are the Consequences for the User When It Fails? | Slides |
| Error Types and Ethics | Reading |
§2.6 Course 1 Summary
| Topic | Type |
|---|---|
| Recapping (Tiny) ML and its Data-Centric Role | Reading |
| Why We Are Excited About TinyML | Slides |
| What We Have Learned Thus Far | Slides |
| What's Coming Next | Slides |
Note
These materials were originally developed for the HarvardX Professional Certificate in Tiny Machine Learning on edX. See the original curriculum for the full item-by-item breakdown including forum prompts and quizzes not listed above.