عنوان
پدید آورنده
موضوع
رده
Q325.5
Q325
.
5
کتابخانه
محل استقرار
INTERNATIONAL STANDARD BOOK NUMBER
Qualification
(epub)
Qualification
(hardback)
Qualification
(paperback)
(Number (ISBN
9781108679930
Erroneous ISBN
9781108470049
Erroneous ISBN
9781108455145
NATIONAL BIBLIOGRAPHY NUMBER
Number
6475
LANGUAGE OF THE ITEM
.Language of Text, Soundtrack etc
انگلیسی
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Mathematics for machine learning
General Material Designation
[electronic resources]
First Statement of Responsibility
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
New York, NY
Name of Publisher, Distributor, etc.
Cambridge University Press
Date of Publication, Distribution, etc.
2019
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
iii, 411 pages.
Other Physical Details
ill., tables.
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index.
CONTENTS NOTE
Text of Note
Introduction and motivation -- Linear algebra -- Analytic geometry -- Matrix decompositions -- Vector calculus -- Probability and distribution -- Continuous optimization -- When models meet data -- Linear regression -- Dimensionality reduction with principal component analysis -- Density estimation with Gaussian mixture models -- Classification with support vector machines.
0
SUMMARY OR ABSTRACT
Text of Note
"The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts"--
TOPICAL NAME USED AS SUBJECT
Entry Element
Machine learning -- Mathematics.
UNCONTROLLED SUBJECT TERMS
Subject Term
Machine learning.
DEWEY DECIMAL CLASSIFICATION
Edition
23
LIBRARY OF CONGRESS CLASSIFICATION
Class number
Q325
.
5
PERSONAL NAME - PRIMARY RESPONSIBILITY
Entry Element
Deisenroth, Marc Peter.
PERSONAL NAME - ALTERNATIVE RESPONSIBILITY
Entry Element
Faisal, A. Aldo.
Entry Element
Ong, Cheng Soon.
ORIGINATING SOURCE
Country
ایران
Agency
Central Library of the Kurdistan University
Date of Transaction
20191218174511.0
Cataloguing Rules (Descriptive Conventions))
rda
e
BL
280328
a
Y
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