Garbage In / Garbage Out: The Importance of Quality Data

Date Thursday, February 18, 12pm ET

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In two thought-provoking presentations, followed by audience discussion, this session will explore the most significant barrier for the implementation of incorporating machine learning in architecture: the availability of quality data. 

This presentation is part of the TAP Building Connections Congress 2021: Machine Learning’s Impact on Architecture and Design.

Learning objectives

  • Explain how data quality is important in supervised learning algorithms. 
  • Explore the challenges of data sources, their supply chain, structure, size and other issues. 
  • Learn how to overcome data quality issues for effective machine learning applications in architecture. 
  • Discuss ways of structuring data for use in machine learning systems. 
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 $40  non-member
 $25  member
1.00 LU
  • RIBA


  • Patrick Chopson, AIA

    Patrick Chopson



    Focusing on the crossover between architecture and technology, Patrick Chopson,
  • Nathan Miller

    Nathan Miller


    Nate Miller is the founder of PROVING GROUND–a digital design agency that enables digital...