Personalized Diagnosis of Symptoms

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prerequisites
intermediate Python • basics of data science • basics of machine learning • basics of NetworkX
skills learned
use NetworkX to jointly represent network structured data and models • learn the weights of a linear classifier using gradient descent • implement a Federated Learning algorithm by combining gradient descent with a simple averaging scheme
Alexander Jung
1 week · 8-10 hours per week · ADVANCED
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In this liveProject, you’ll apply the federated learning machine learning paradigm to compute a personalized infection risk diagnosis for COVID-19. Federated learning is an ML paradigm that learns from decentralized data via distributed computing environments. The risk diagnosis will come from a tailored classifier that is trained separately for each individual, by combining gradient descent with a network averaging method. You’ll then develop a federate learning algorithm that can be implemented by scalable passing messages over the contact network.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

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 (logistic regression, gradient descent, using networks, distributed algorithms to compute gradients)

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