|Eingestellt in Kategorie:
Dieser Artikel ist nicht mehr vorrätig.

Machine Learning verstehen - Hardcover, von Shalev-Shwartz Shai - sehr gut-

Ursprünglicher Text
Understanding Machine Learning - Hardcover, by Shalev-Shwartz Shai - Very Good
BooksRun
(162837)
Angemeldet als gewerblicher Verkäufer
US $39,03
Ca.EUR 33,31
Artikelzustand:
Sehr gut
Versand:
Kostenlos USPS Media MailTM.
Standort: Philadelphia, Pennsylvania, USA
Lieferung:
Lieferung zwischen Fr, 29. Aug und Fr, 5. Sep nach 94104 bei heutigem Zahlungseingang
Wir wenden ein spezielles Verfahren zur Einschätzung des Liefertermins an – in diese Schätzung fließen Faktoren wie die Entfernung des Käufers zum Artikelstandort, der gewählte Versandservice, die bisher versandten Artikel des Verkäufers und weitere ein. Insbesondere während saisonaler Spitzenzeiten können die Lieferzeiten abweichen.
Rücknahme:
30 Tage Rückgabe. Kostenloser Rückversand.
Zahlungen:
   Diners Club 

Sicher einkaufen

eBay-Käuferschutz
Geld zurück, wenn etwas mit diesem Artikel nicht stimmt. Mehr erfahreneBay-Käuferschutz - wird in neuem Fenster oder Tab geöffnet

  • Gratis Rückversand im Inland
  • Punkte für jeden Kauf und Verkauf
  • Exklusive Plus-Deals
Der Verkäufer ist für dieses Angebot verantwortlich.
eBay-Artikelnr.:404431034811
Zuletzt aktualisiert am 25. Aug. 2025 02:09:48 MESZAlle Änderungen ansehenAlle Änderungen ansehen

Artikelmerkmale

Artikelzustand
Sehr gut: Buch, das nicht neu aussieht und gelesen wurde, sich aber in einem hervorragenden Zustand ...
Book Title
Understanding Machine Learning
ISBN
9781107057135

Über dieses Produkt

Product Identifiers

Publisher
Cambridge University Press
ISBN-10
1107057132
ISBN-13
9781107057135
eBay Product ID (ePID)
171820749

Product Key Features

Number of Pages
410 Pages
Publication Name
Understanding Machine Learning : from Theory to Algorithms
Language
English
Publication Year
2014
Subject
Algebra / General, Computer Vision & Pattern Recognition
Type
Textbook
Subject Area
Mathematics, Computers
Author
Shai Ben-David, Shai Shalev-Shwartz
Format
Hardcover

Dimensions

Item Height
1.1 in
Item Weight
32.2 Oz
Item Length
10.2 in
Item Width
7.2 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2014-001779
Reviews
Advance praise: 'This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data.' Bernhard Schölkopf, Max Planck Institute for Intelligent Systems, "This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data." Bernhard Schlkopf, Max Planck Institute for Intelligent Systems
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/1
Table Of Content
1. Introduction; Part I. Foundations: 2. A gentle start; 3. A formal learning model; 4. Learning via uniform convergence; 5. The bias-complexity trade-off; 6. The VC-dimension; 7. Non-uniform learnability; 8. The runtime of learning; Part II. From Theory to Algorithms: 9. Linear predictors; 10. Boosting; 11. Model selection and validation; 12. Convex learning problems; 13. Regularization and stability; 14. Stochastic gradient descent; 15. Support vector machines; 16. Kernel methods; 17. Multiclass, ranking, and complex prediction problems; 18. Decision trees; 19. Nearest neighbor; 20. Neural networks; Part III. Additional Learning Models: 21. Online learning; 22. Clustering; 23. Dimensionality reduction; 24. Generative models; 25. Feature selection and generation; Part IV. Advanced Theory: 26. Rademacher complexities; 27. Covering numbers; 28. Proof of the fundamental theorem of learning theory; 29. Multiclass learnability; 30. Compression bounds; 31. PAC-Bayes; Appendix A. Technical lemmas; Appendix B. Measure concentration; Appendix C. Linear algebra.
Synopsis
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering., Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering., Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the 'hows' and 'whys' of machine-learning algorithms, making the field accessible to both students and practitioners.
LC Classification Number
Q325.5 .S475 2014

Artikelbeschreibung des Verkäufers

Rechtliche Informationen des Verkäufers

Ich versichere, dass alle meine Verkaufsaktivitäten in Übereinstimmung mit allen geltenden Gesetzen und Vorschriften der EU erfolgen.
Info zu diesem Verkäufer

BooksRun

99,2% positive Bewertungen875.477 Artikel verkauft

Mitglied seit Aug 2014
Angemeldet als gewerblicher Verkäufer
BooksRun is an online seller of new and used books and textbooks. Best prices for books since 2014, we're a one-stop shop for all sorts of books, from fiction to textbooks. We're constantly expanding ...
Mehr anzeigen
Shop besuchenKontakt

Detaillierte Verkäuferbewertungen

Durchschnitt in den letzten 12 Monaten
Genaue Beschreibung
4.9
Angemessene Versandkosten
5.0
Lieferzeit
5.0
Kommunikation
5.0

Beliebte Kategorien in diesem Shop

Verkäuferbewertungen (180.412)

Alle Bewertungen
Positiv
Neutral
Negativ
    • _***c (381)- Bewertung vom Käufer.
      Letzte 6 Monate
      Bestätigter Kauf
      Smooth transaction, authentic item, happy buyer, A+++ seller!
    Alle Bewertungen ansehen