A procedure learns when it turns a finite sample into a map and that map is scored on inputs it did not use. The object is the pair of a hypothesis class and an estimation rule, together with a claim about unseen data.
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Lesson close · Foundations of Machine Learning
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Sequence
Continue to Learning Paradigms, the next prerequisite step in this course.
Assume the pairs are i.i.d. draws from an unknown law on , and fix a loss . Risk is
Success means is small. A rule-based system is the case where a person writes and ignores .
The usual chain has five steps, and later model families only change step 1 or 3.
Expert systems fixed by hand, so step 3 was empty. Early neural nets used the same empirical-risk step on small . Kernels and ensembles enlarged while keeping a convex or tree-growing optimizer. Large samples and hardware made deep compositions the default flexible class for images, text, and some sequences. On tabular features with moderate , a line or a tree often wins because a smaller keeps from exploding. Depth does not replace the risk or the holdout.
A cross-sectional factor model is a supervised map from characteristics to a return. The definition requires a later window, or a purged holdout, before anyone calls the fit an edge.
Interactive · ML eras slider
focus: scale
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