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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
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
features
- Self-paced
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- 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.