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Machine Learning

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The field of machine learning is introduced at a conceptual level. Ideas such as supervised and unsupervised as well as regression and classification are explained. The tradeoff between bias, variance, and model complexity is discussed as a central guiding idea of learning. Various types of model that machine learning can produce are introduced such as the neural network (feed-forward and recurrent), support vector machine, random forest, self-organizing map, and Bayesian network. Training a model is discussed next with its main ideas of splitting a dataset into training, testing, and validation sets as well as performing cross-validation.

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Ketersediaan

79237.1

006.31 MIT M

Library Lantai 3

Tersedia

Informasi Detil

Judul Seri

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No. Panggil

006.31 MIT M

Penerbit

Singapore : McGraw-Hill., 1997

Deskripsi Fisik

413 hlm

Bahasa

English

ISBN/ISSN

0-07-115467-1

Klasifikasi

NONE

Tipe Isi

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Tipe Media

-

Tipe Pembawa

-

Edisi

1

Subyek

construct computer program

Info Detil Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

Lampiran Berkas

Komentar

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