Elementos da teoria de aprendizagem de m\'aquina supervisionada
Abstract
This is a set of lecture notes for an introductory course (advanced undergaduates or the 1st graduate course) on foundations of supervised machine learning (in Portuguese). The topics include: the geometry of the Hamming cube, concentration of measure, shattering and VC dimension, Glivenko-Cantelli classes, PAC learnability, universal consistency and the k-NN classifier in metric spaces, dimensionality reduction, universal approximation, sample compression. There are appendices on metric and normed spaces, measure theory, etc., making the notes self-contained. Este \'e um conjunto de notas de aula para um curso introdut\'orio (curso de graduac\~ao avancado ou o 1o curso de p\'os) sobre fundamentos da aprendizagem de m\'aquina supervisionada (em Portugu\es). Os t\'opicos incluem: a geometria do cubo de Hamming, concentrac\~ao de medida, fragmentac\~ao e dimens\~ao de Vapnik-Chervonenkis, classes de Glivenko-Cantelli, aprendizabilidade PAC, consist\encia universal e o classificador k-NN em espacos m\'etricos, reduc\~ao de dimensionalidade, aproximac\~ao universal, compress\~ao amostral. H\'a ap\endices sobre espacos m\'etricos e normados, teoria de medida, etc., tornando as notas autosuficientes.
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