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
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.
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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:
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