From the course: Grasshopper: Generative Design for Architecture
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Strengths and limitations of machine learning solvers
From the course: Grasshopper: Generative Design for Architecture
Strengths and limitations of machine learning solvers
- [Instructor] With machine learning, algorithms start to respond to the real world, the way people do. With regression, machine learning can use prior experiences to predict future events, without understanding the details of how the system is working. With clustering, machine learning can heuristically group similar instances together based on their characteristics, just as we constantly categorize the world around us. With classification, machine learning can be taught to recognize specific types within messy data, a capability that now supports language recognition among many other emerging technologies. With all these capabilities, though, there are trade-offs, and each technique lends itself to specific scenarios. When determining when to use machine learning tools, the operative term is learning. If you have a problem that's fully understood, the learning has already happened. It's usually easier and faster to describe the known solution to a machine, writing an algorithm, than…
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