Federated Learning

Build Network Models for Pandemics you own this product

This project is part of the liveProject series Federated Learning Over Networks for Pandemics
prerequisites
intermediate Python • basics of data science • basics of machine learning • basics of NetworkX
skills learned
reading in location recordings from a CSV file • determining geodesic distances between locations • implement a simple algorithm that computes a summary statistic of networked data
Alexander Jung
1 week · 8-10 hours per week · ADVANCED
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liveProject This project is part of the liveProject series Federated Learning Over Networks for Pandemics liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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Look inside
In this liveProject, you’ll use machine learning to construct a contact tracing network for COVID-19 using location recordings from smart phone data. You’ll read the location of infected individuals, and generate a contact network of individuals who have been within two meters. Once you’ve established this tracing system, you’ll implement a distributed algorithm that can compute the average infection rate for each connected component of the contact network.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Alexander Jung
Alexander Jung is an assistant professor for machine learning at Aalto University in Finland. Prior to joining Aalto, he obtained a PhD in statistical signal processing from TU Vienna in 2012 and was a postdoc at TU Vienna and ETH Zurich. Alex leads the Aalto research group “Machine Learning for Big Data'' that studies the fundamental limits and efficient algorithms for machine learning from large distributed collections of data. His current research focus is on privacy preserving and explainable federated machine learning methods for big data over networks. Alex has developed some of the most popular courses at Aalto University. He was selected as the Teacher of the Year by the Department of Computer Science in 2018.

prerequisites

This liveProject is for Python data scientists interested in applying big data analytics to public healthcare. To begin this liveProject you will need to be familiar with the following:

TOOLS
  • Intermediate Python (declaring variables, loops, branches, debugging, importing modules)
  • Basics of Matplotlib
  • Basics of NumPy
  • Basics of GeoPy
  • Basics of NetworkX
TECHNIQUES
  • Basics of data science

you will learn

In this liveProject, you’ll learn how to represent contact networks, and how a network representation lends naturally to efficient algorithms for processing data during pandemics. network. These distributed algorithms are an excellent choice for privacy-preserving machine learning that can be implemented without sharing any local raw data.

  • Reading in location recordings from a CSV file
  • Determining geodesic distances between locations that are specified by a latitude and a longitude
  • Implement a simple algorithm that computes a summary statistic of networked data

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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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