This repository contains the lab materials for Connected Environments DL4Sn module. The code folders are divided into the weekly activities. A summary overview of the course is below with a more detailed overview on the UCL Moodle site for CASA0018.

We suggest that students take a fork of this repository so that they can add their own work in progress as they work through the material.

Our environment is increasingly being connected with small computers that are aware and responsive. This introductory, hands on module will introduce students to machine learning applied to low power embedded devices. Students will learn the main concepts of deep learning, understand how to apply deep learning to data streams from cameras and other IoT sensors, and how to deploy AI onto sensor devices, such as mobile phones and microcontrollers. Students will learn about deep learning architectures for image and time series data and will apply these ideas to sensor data in order to do forecasting, image recognition, and object tracking. A significant component of the module will be an individual project to build and deploy an intelligent sensor application. Students will practice these ideas using Python and TensorFlow. The programme has been developed with support from the Google TensorFlow team, uses the TinyML book as a core text and utilises the Arduino Nano as the primary prototyping platform.

On completion, students will be able to:

Domain Knowledge

• Understand AI / machine learning terminology
• Understand deep learning opportunities and limitations
• Understand different types of deep learning models

Prototyping Skills

• Implement deep learning models in Python
• Prepare data for model training
• Select and train suitable models for different use cases (video & timeseries)
• Embed AI on sensor devices, such as a mobile phone or a microcontroller.

Collaboration

• Document and share project information to support reproducible research
• Provide peer feedback to fellow students on project work
• Present design decisions and prototypes to receive critical feedback

There is a course reading list under the ReadingLists@UCL facilty which can be accessed here: (https://ucl.rl.talis.com/modules/casa0018.html)

The core text for the module is TinyML by Pete Warden and Daniel Situnayake

We also reference

• Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

(2500 word equiv)

• project build (30%),
• github page - code / docs / photos / video (30%),
• crit (40%)