Garbage In / Garbage Out: The Importance of Quality Data
From Building Connections Congress 2021: Machine Learning’s Impact on Architecture and Design hosted by the AIA Technology in Architectural Practice (TAP) Knowledge Community. This course explores the most significant barrier for the implementation of incorporating machine learning in architecture: the availability of quality data.
- 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.
This session was recorded live on February 18, 2021.