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Educational Design Contest 2024/25

Course materials for teaching Machine Learning Systems with TinyML

Description:

This submission provides a comprehensive set of materials for teaching TinyML and machine learning systems. Below is a breakdown of what each resource offers:

• Google Drive (Slides Repository):
  This folder contains all the slides, which can be downloaded, edited, and modified as needed for teaching TinyML.
  Slides Repository
• GitHub (Syllabus & Course Structure):
  This repository includes the syllabus and the structured framework for integrating these slides into the classroom. It provides guidance on how the materials can be effectively incorporated into a course.
  TinyML Syllabus & Courseware
• Machine Learning Systems Book (PDF & Online):
  This book serves as a detailed reading resource for students, introducing the fundamental principles of machine learning systems. A key focus is on understanding ML systems as a discipline, independent of whether they are large-scale or resource-constrained. This approach is similar to how operating systems are taught—whether large or embedded, the core principles remain the same. While we specialize in teaching TinyML, our philosophy emphasizes the broader principles of machine learning systems.
  PDF Download: Machine Learning Systems Book (Open Source)
  Online Version: Machine Learning Systems
• Audio Podcast (Cover Page):
  Additional insights and discussions related to the book and ML systems are available in the podcast.
  Podcast & More Resources

These resources are openly available for educators, students, and anyone interested in learning or teaching TinyML and machine learning systems.

Organisations: Harvard University

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4 resources for this course

This submission provides a comprehensive set of materials for teaching TinyML and machine learning systems. Below is a breakdown of what each resource offers:

• Google Drive (Slides Repository):
  This folder contains all the slides, which can be downloaded, edited, and modified as needed for teaching TinyML.
  Slides Repository
• GitHub (Syllabus & Course Structure):
  This repository includes the syllabus and the structured framework for integrating these slides into the classroom. It provides guidance on how the materials can be effectively incorporated into a course.
  TinyML Syllabus & Courseware
• Machine Learning Systems Book (PDF & Online):
  This book serves as a detailed reading resource for students, introducing the fundamental principles of machine learning systems. A key focus is on understanding ML systems as a discipline, independent of whether they are large-scale or resource-constrained. This approach is similar to how operating systems are taught—whether large or embedded, the core principles remain the same. While we specialize in teaching TinyML, our philosophy emphasizes the broader principles of machine learning systems.
  PDF Download: Machine Learning Systems Book (Open Source)
  Online Version: Machine Learning Systems
• Audio Podcast (Cover Page):
  Additional insights and discussions related to the book and ML systems are available in the podcast.
  Podcast & More Resources

These resources are openly available for educators, students, and anyone interested in learning or teaching TinyML and machine learning systems.

People

Organisations: Harvard University

Creators:	Vijay Janapa Reddi

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URL: https://mlsysbook.ai

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