Machine learning refined : foundations, algorithms, and applications / Jeremy Watt, Northwestern University, Illinois, Reza Borhani, Northwestern University, Illinois, Aggelos K. Katsaggelos, Northwestern University, Illinois.

By:

Watt, Jeremy [author.]

Contributor(s):

Material type: Text

TextPublisher: New York : Cambridge University Press, 2020Copyright date: © Cambridge University Press 2020Edition: Second EditionDescription: 1 Electronic ResourcesContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9781108480727

Subject(s):

Machine learning

Additional physical formats: Online version:: Machine learning refinedDDC classification:

006.31 WAT 2020 23

LOC classification:

Q325.5 .W38 2020

Online resources:

Click here to access online

Summary: "The second edition of this text is a complete revision of our first endeavor, with virtually every chapter of the original rewritten from the ground up and eight new chapters of material added, doubling the size of the first edition. Topics from the first edition, from expositions on gradient descent to those on One-versus- All classification and Principal Component Analysis have been reworked and polished. A swath of new topics have been added throughout the text, from derivative-free optimization to weighted supervised learning, feature selection, nonlinear feature engineering, boosting-based cross-validation, and more"-- Provided by publisher.

List(s) this item appears in: eBooks_FEC

Average rating: 0.0 (0 votes)

Holdings ( 1 )
Title notes ( 3 )

Holdings
Item type	Current library	Home library	Call number	Status	Date due	Barcode	Item holds
Electronic Book	FIRST CITY UNIVERSITY COLLEGE	FIRST CITY UNIVERSITY COLLEGE	006.31 WAT 2020 (Browse shelf(Opens below))	e-book		e00063

Total holds: 0

First edition published 2016.

Includes bibliographical references and index.

"The second edition of this text is a complete revision of our first endeavor, with virtually every chapter of the original rewritten from the ground up and eight new chapters of material added, doubling the size of the first edition. Topics from the first edition, from expositions on gradient descent to those on One-versus- All classification and Principal Component Analysis have been reworked and polished. A swath of new topics have been added throughout the text, from derivative-free optimization to weighted supervised learning, feature selection, nonlinear feature engineering, boosting-based cross-validation, and more"-- Provided by publisher.