Fundamentals of TinyML

Course Description

Fundamentals of TinyML is an introductory course offered by HarvardX that delves into the exciting world of Tiny Machine Learning (TinyML), one of the fastest-growing areas in Deep Learning. This course provides a comprehensive foundation for understanding the emerging field of TinyML, which sits at the intersection of embedded Machine Learning applications, algorithms, hardware, and software.

Unlike mainstream machine learning, TinyML requires expertise in both software and embedded hardware. The course focuses on the basics of machine learning, deep learning, and embedded devices and systems, such as smartphones and other tiny devices. Students will learn essential data science techniques for collecting data and develop an understanding of learning algorithms to train basic machine learning models.

What You'll Learn

  • Fundamentals of Machine Learning (ML)
  • Fundamentals of Deep Learning
  • Data collection techniques for ML
  • Training and deploying ML models
  • Understanding embedded ML
  • Responsible AI Design
  • The Machine Learning Paradigm
  • Building blocks of Deep Learning
  • Exploring Machine Learning Scenarios
  • Building a Computer Vision Model

Prerequisites

Basic scripting in Python is a prerequisite for this course. Familiarity with basic programming concepts will be beneficial for understanding and implementing the course material.

Course Content

  • Introduction to TinyML and its challenges
  • The future of ML and its applications in tiny devices
  • Machine Learning paradigms and algorithms
  • Deep Learning fundamentals and architectures
  • Data collection and preparation techniques for TinyML
  • Training and deploying machine learning models on embedded systems
  • Computer vision applications in TinyML
  • Responsible AI design principles and practices
  • Embedded systems and their role in TinyML
  • TinyML applications and real-world use cases

Who This Course is For

  • Data scientists looking to expand their knowledge into embedded ML
  • Embedded systems engineers interested in implementing ML on tiny devices
  • Computer science students exploring new areas of AI and ML
  • IoT developers wanting to incorporate ML into their projects
  • Anyone curious about the future of AI in small-scale devices

Real-World Applications

  • Developing smart IoT devices with on-device intelligence
  • Creating energy-efficient ML solutions for wearable technology
  • Implementing computer vision applications on smartphones and tiny cameras
  • Designing voice recognition systems for small-scale devices
  • Optimizing ML models for resource-constrained environments
  • Building predictive maintenance systems for industrial equipment
  • Developing personalized healthcare monitoring devices
  • Creating intelligent environmental sensors for agriculture and smart cities
  • Implementing ML-based security systems on embedded devices
  • Designing autonomous systems for robotics and drones

Syllabus

Chapter 1: Welcome to TinyML

  • Course Overview
  • The Future of ML is Tiny and Bright
  • TinyML Challenges
  • Getting Started

Chapter 2: Introduction to (Tiny) ML

  • The Machine Learning Paradigm
  • The Building Blocks of Deep Learning
  • Exploring Machine Learning Scenarios
  • Building a Computer Vision Model
  • Responsible AI Design
  • Summary

Additional Information

This course is part of the TinyML Professional Certificate program, which includes additional courses on Applications of TinyML and Deploying TinyML. While Fundamentals of TinyML provides an excellent introduction to the field, it is not a prerequisite for the other courses in the program for those with sufficient machine learning and embedded systems experience.

By enrolling in this course, students will gain a solid understanding of the "language" behind TinyML and be well-prepared to explore more advanced topics in this rapidly growing field. The knowledge and skills acquired will be invaluable for those looking to innovate in the realm of embedded machine learning and contribute to the development of intelligent, resource-efficient devices that can transform various industries and improve our daily lives.