Statistical mechanics in biology and biocomputation
Having covered the very basics of machine learning (ML) in a previous post, I want to introduce you to an essential conceptual framework embodied in a simple graph called the “learning curve” or “training curve.” This graph can be made to aid evaluation of any ML project, and is invaluable for non-expert evaluation of a project. The learning curve is closely related to cross-validation, which we will also discuss.
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I want to introduce machine learning (ML) to people outside the field, both non-mathematical scientists totally new to machine learning and quantitative folks who are novices. My qualifications for this are that I’m an outsider to ML myself, maybe an “advanced beginner” – with several years of experience. As I have learned ML, I try to keep an eye out for what’s important and what isn’t.
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