Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers.
Introduction to Embedded Machine Learning
Introduction to Embedded Machine Learning
Instructors: Shawn Hymel
Access provided by American University of Bahrain
53,825 already enrolled
Gain insight into a topic and learn the fundamentals.
751 reviews
Intermediate level
Recommended experience
Flexible schedule
2 weeks at 10 hours a week
Learn at your own pace
96%
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What you'll learn
The basics of a machine learning system
How to deploy a machine learning model to a microcontroller
How to use machine learning to make decisions and predictions in an embedded system
Skills you'll gain
- Convolutional Neural Networks
- Digital Signal Processing
- Applied Machine Learning
- Data Ethics
- Deep Learning
- Embedded Software
- Data Preprocessing
- Machine Learning
- Embedded Systems
- Data Analysis
- Artificial Intelligence and Machine Learning (AI/ML)
- Model Deployment
- Artificial Neural Networks
- Computer Programming
- Model Evaluation
- Feature Engineering
Details to know
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Assessments
14 assignments
Taught in English
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There are 3 modules in this course
In this module, we will introduce the concept of machine learning, how it can be used to solve problems, and its limitations. We will also cover how machine learning on embedded systems, such as single board computers and microcontrollers, can be effectively used to solve problems and create new types of computer interfaces. Then, we will introduce the Edge Impulse tool and collect motion data for a "magic wand" demo. Finally, we will examine the various features that can be calculated from this raw motion data, including root mean square (RMS), Fourier transform, and power spectral density (PSD).
What's included
13 videos15 readings5 assignments2 discussion prompts
In this module, we will look at how neural networks work, how to train them, and how to use them to perform inference in an embedded system. We will continue the previous demo of creating a motion classification system using motion data collected from a smartphone or Arduino board. Finally, we will challenge you with a new motion classification project where you will have the opportunity to implement the concepts learning in this module and the previous module.
What's included
10 videos10 readings5 assignments1 discussion prompt
In this module, we cover audio classification on embedded systems. Specifically, we will go over the basics of extracting mel-frequency cepstral coefficients (MFCCs) as features from recorded audio, training a convolutional neural network (CNN) and deploying that neural network to a microcontroller. Additionally, we dive into some of the implementation strategies on embedded systems and talk about how machine learning compares to sensor fusion.
What's included
9 videos7 readings4 assignments1 discussion prompt1 plugin
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Showing 3 of 751
SJ
Reviewed on Aug 18, 2021
Awesome course for beginners. I don't know how much of my background helped make this awesome, but it is awesome.
JZ
Reviewed on Apr 8, 2021
This is a perfect and practical introduction to embedded machine learning. Learned a lot! Thank you.
AP
Reviewed on Apr 19, 2022
i like the way course is designed.i tried all project explained in course without re-viewing cource material.
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