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Probability and statistics for machine learning : (Record no. 140278)

MARC details
000 -LEADER
fixed length control field 02503nam a22002297a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260715162247.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 260331b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783031532818 (C.1) hbk
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783031532849 (C.2) pbk
040 ## - CATALOGING SOURCE
Transcribing agency PK-LaUMT
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.2
Item number AGG-P
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Aggarwal, Charu C.
245 10 - TITLE STATEMENT
Title Probability and statistics for machine learning :
Remainder of title a textbook /
Statement of responsibility, etc Charu C Aggarwal
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Switzerland :
Name of publisher, distributor, etc Springer,
Date of publication, distribution, etc 2024
300 ## - PHYSICAL DESCRIPTION
Extent 522 p.
500 ## - GENERAL NOTE
General note Includes bibliographical references and index.
520 ## - SUMMARY, ETC.
Summary, etc "This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories: 1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its relationship to machine learning. The fundamentals of probability and statistics are covered in Chapters 2 through 5. 2. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner. Chapters 6 through 9 explore how different models from probability and statistics are applied to machine learning. Perhaps the most important tool that bridges the gap from data to probability is maximum-likelihood estimation, which is a foundational concept from the perspective of machine learning. This concept is explored repeatedly in these chapters. 3. Advanced topics: Chapter 10 is devoted to discrete-state Markov processes. It explores the application of probability and statistics to a temporal and sequential setting, although the applications extend to more complex settings such as graphical data. Chapter 11 covers a number of probabilistic inequalities and approximations. The style of writing promotes the learning of probability and statistics simultaneously with a probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners."--Back cover
546 ## - LANGUAGE NOTE
Language note Eng
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Probabilities
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical statistics
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Damaged status Home library Current library Date acquired Full call number Barcode Date last seen Price effective from Koha item type
      UMT Main Campus UMT Main Campus 2026-03-31 519.2 AGG-P 152250 2026-05-13 2026-03-31 Books
      UMT Main Campus UMT Main Campus 2026-07-15 519.2 AGG-P 153351 2026-07-15 2026-07-15 Books