In this introductory lecture, Nate Miller reviews basic Machine Learning concepts and examples in the context of architectural design. Nate discusses definitions for machine learning and provides example uses of evolutionary solvers, clustering algorithms, regression, and probabilistic solvers that can be applied to help with design decisions.
This lecture was recorded as part of the first week of workshops for Proving Ground’s summer research interns in 2019.
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