Bild 1 von 1
Bild 1 von 1
Ein erster Kurs in maschinellem Lernen (Chapman & Hall/CRC Machine Learning & Patte,-
US $91,99
Ca.EUR 86,12
Artikelzustand:
“Used book in good condition. Shows typical wear. Quick shipping. Satisfaction guaranteed!”
Versand:
Standort: Multiple Locations, USA
Lieferung:
Lieferung zwischen Do, 27. Jun und Mo, 1. Jul nach 43230 bei heutigem Zahlungseingang
Rücknahmen:
Zahlungen:
Sicher einkaufen
Der Verkäufer ist für dieses Angebot verantwortlich.
eBay-Artikelnr.:166683948257
Artikelmerkmale
- Artikelzustand
- Gut
- Hinweise des Verkäufers
- “Used book in good condition. Shows typical wear. Quick shipping. Satisfaction guaranteed!”
- ISBN
- 9781498738484
- Subject Area
- Computers
- Publication Name
- First Course in Machine Learning
- Publisher
- CRC Press LLC
- Item Length
- 9.2 in
- Subject
- Programming / Games
- Publication Year
- 2016
- Series
- Chapman and Hall/Crc Machine Learning and Pattern Recognition Ser.
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 1.3 in
- Item Weight
- 28 Oz
- Item Width
- 6.5 in
- Number of Pages
- 397 Pages
Über dieses Produkt
Product Information
The new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models.
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
1498738486
ISBN-13
9781498738484
eBay Product ID (ePID)
223746734
Product Key Features
Number of Pages
397 Pages
Language
English
Publication Name
First Course in Machine Learning
Publication Year
2016
Subject
Programming / Games
Type
Textbook
Subject Area
Computers
Series
Chapman and Hall/Crc Machine Learning and Pattern Recognition Ser.
Format
Hardcover
Dimensions
Item Height
1.3 in
Item Weight
28 Oz
Item Length
9.2 in
Item Width
6.5 in
Additional Product Features
Edition Number
2
Intended Audience
College Audience
Dewey Edition
22
Reviews
"The new edition of A First Course in Machine Learningby Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing 'just in time' the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts. One of the strengths of the book is its practical approach. An extensive collection of code written in MATLAB/Octave, R, and Python is available from an associated web page that allows the reader to change models and parameter values to make [it] easier to understand and apply these models in real applications. The authors [also] introduce more advanced, state-of-the-art machine learning methods, such as Gaussian process models and advanced mixture models, which are used across machine learning. This makes the book interesting not only to students with little or no background in machine learning but also to more advanced graduate students interested in statistical approaches to machine learning." --Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark "A First Course in Machine Learningby Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC." --Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade." --Daniel Barbara, George Mason University, Fairfax, Virginia, USA "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength. While there are other books available that aim for completeness, with exhaustively comprehensive introductions to every branch of machine learning, the book by Rogers and Girolami starts with the basics, builds a solid and logical foundation of methodology, before introducing some more advanced topics. The essentials of the model construction, validation, and evaluation process are communicated clearly and in such a manner as to be accessible to the student taking such a course. I was also pleased to see that the authors have not shied away from producing algebraic derivations throughout, which are for many students an essential part of the learning process--many other texts omit such details, leaving them as 'an exercise for the reader.' Being shown the explicit steps required for such derivations is an important part of developing a sense of confidence in the student. Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." --David Clifton, University of Oxford, UK, "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength. While there are other books available that aim for completeness, with exhaustively comprehensive introductions to every branch of machine learning, the book by Rogers and Girolami starts with the basics, builds a solid and logical foundation of methodology, before introducing some more advanced topics. The essentials of the model construction, validation, and evaluation process are communicated clearly and in such a manner as to be accessible to the student taking such a course. I was also pleased to see that the authors have not shied away from producing algebraic derivations throughout, which are for many students an essential part of the learning process--many other texts omit such details, leaving them as 'an exercise for the reader.' Being shown the explicit steps required for such derivations is an important part of developing a sense of confidence in the student. Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." --David Clifton, University of Oxford, UK "In my opinion, this is by far the best introduction to Machine Learning. It accomplishes something I would think impossible: it assumes essentially only high school mathematics and no statistics background, and yet, by introducing math, probability and statistics as needed, it manages to do an entirely rigorous introduction to Machine Learning. Proofs are not provided only for very few theorems; the book goes fairly deep and is really enjoyable to read. I told my students that this book will be one of the best investments they have ever made!" --Aleksandar Ignjatovic, University of New South Wales "The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduct, "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength. While there are other books available that aim for completeness, with exhaustively comprehensive introductions to every branch of machine learning, the book by Rogers and Girolami starts with the basics, builds a solid and logical foundation of methodology, before introducing some more advanced topics. The essentials of the model construction, validation, and evaluation process are communicated clearly and in such a manner as to be accessible to the student taking such a course. I was also pleased to see that the authors have not shied away from producing algebraic derivations throughout, which are for many students an essential part of the learning process--many other texts omit such details, leaving them as 'an exercise for the reader.' Being shown the explicit steps required for such derivations is an important part of developing a sense of confidence in the student. Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." --David Clifton, University of Oxford, UK, "This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning. The prerequisites on math or statistics are minimal and following the content is a fairly easy process. The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective." --Guangzhi Qu, Oakland University, Rochester, Michigan, USA "The new edition of A First Course in Machine Learningby Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing 'just in time' the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts. One of the strengths of the book is its practical approach. An extensive collection of code written in MATLAB/Octave, R, and Python is available from an associated web page that allows the reader to change models and parameter values to make [it] easier to understand and apply these models in real applications. The authors [also] introduce more advanced, state-of-the-art machine learning methods, such as Gaussian process models and advanced mixture models, which are used across machine learning. This makes the book interesting not only to students with little or no background in machine learning but also to more advanced graduate students interested in statistical approaches to machine learning." --Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark "A First Course in Machine Learningby Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC." --Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade." --Daniel Barbara, George Mason University, Fairfax, Virginia, USA "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength. While there are other books available that aim for completeness, with exhaustively comprehensive introductions to every branch of machine learning, the book by Rogers and Girolami starts with the basics, builds a solid and logical foundation of methodology, before introducing some more advanced topics. The essentials of the model construction, validation, and evaluation process are communicated clearly and in such a manner as to be accessible to the student taking such a course. I was also pleased to see that the authors have not shied away from producing algebraic derivations throughout, which are for many students an essential part of the learning process--many other texts omit such details, leaving them as 'an exercise for the reader.' Being shown the explicit steps required for such derivations is an important part of developing a sense of confidence in the student. Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." --David Clifton, University of Oxford, UK, "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade." --Daniel Barbara, George Mason University, Fairfax, Virginia, USA "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength. While there are other books available that aim for completeness, with exhaustively comprehensive introductions to every branch of machine learning, the book by Rogers and Girolami starts with the basics, builds a solid and logical foundation of methodology, before introducing some more advanced topics. The essentials of the model construction, validation, and evaluation process are communicated clearly and in such a manner as to be accessible to the student taking such a course. I was also pleased to see that the authors have not shied away from producing algebraic derivations throughout, which are for many students an essential part of the learning process--many other texts omit such details, leaving them as 'an exercise for the reader.' Being shown the explicit steps required for such derivations is an important part of developing a sense of confidence in the student. Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." --David Clifton, University of Oxford, UK
Illustrated
Yes
Dewey Decimal
006.31
Edition Description
Revised Edition,New Edition
Lc Classification Number
Q325.5.R64 2016
Table of Content
Linear Modelling: A Least Squares Approach. Linear Modelling: A Maximum Likelihood Approach. The Bayesian Approach to Machine Learning. Bayesian Inference. Classification. Clustering. Principal Components Analysis and Latent Variable Models. Further Topics in Markov Chain Monte Carlo. Classification and Regression with Gaussian Processes. Dirichlet Process models.
Copyright Date
2017
Artikelbeschreibung des Verkäufers
Rechtliche Informationen des Verkäufers
Rocky Mountain Textbooks inc.
Timothy Turner
425 W 8th St
81003 Pueblo, CO
United States
Ich versichere, dass alle meine Verkaufsaktivitäten in Übereinstimmung mit allen geltenden Gesetzen und Vorschriften der EU erfolgen.
Der Verkäufer ist für dieses Angebot verantwortlich.
eBay-Artikelnr.:166683948257
Verpackung und Versand
Artikelstandort:
Multiple Locations, USA
Versand nach:
Afghanistan, Albanien, Andorra, Anguilla, Antigua und Barbuda, Armenien, Aruba, Aserbaidschan, Australien, Bahamas, Bangladesch, Belgien, Belize, Bermuda, Bhutan, Bosnien und Herzegowina, Brunei Darussalam, Bulgarien, China, Costa Rica, Deutschland, Dominikanische Republik, Dänemark, El Salvador, Estland, Fidschi, Finnland, Frankreich, Georgien, Gibraltar, Grenada, Griechenland, Grönland, Guatemala, Haiti, Honduras, Hongkong, Island, Israel, Jamaika, Japan, Kaimaninseln, Kambodscha, Kanada, Kirgisistan, Kiribati, Laos, Lettland, Liechtenstein, Litauen, Luxemburg, Macau, Malaysia, Malediven, Mazedonien, Moldawien, Monaco, Mongolei, Montenegro, Montserrat, Nauru, Nepal, Neuseeland, Nicaragua, Niederlande, Norwegen, Panama, Papua-Neuguinea, Polen, Portugal, Rumänien, Salomonen, Samoa (Westsamoa), San Marino, Schweden, Schweiz, Serbien, Slowakei, Slowenien, Spanien, St. Kitts und Nevis, St. Lucia, St. Vincent und die Grenadinen, Südkorea, Tadschikistan, Taiwan, Thailand, Tonga, Trinidad und Tobago, Turkmenistan, Turks- und Caicosinseln, USA, Ungarn, Usbekistan, Vanuatu, Vatikan, Vietnam, Wallis und Futuna, Zypern, Österreich
Ausgeschlossen:
Afrika, Bahrain, Barbados, Französisch-Guayana, Französisch-Polynesien, Guadeloupe, Indien, Indonesien, Irak, Irland, Italien, Jemen, Jordanien, Kasachstan, Katar, Kuwait, Libanon, Libyen, Malta, Martinique, Mexiko, Neukaledonien, Oman, Pakistan, Philippinen, Republik Kroatien, Russische Föderation, Réunion, Saudi-Arabien, Singapur, Sri Lanka, Südamerika, Tschechische Republik, Türkei, US-Protektorate, Ukraine, Venezuela, Vereinigte Arabische Emirate, Vereinigtes Königreich
| Verpackung und Versand | Nach | Service | Lieferung*Siehe Lieferhinweise |
|---|---|---|---|
| US $9,99 (ca. EUR 9,35) | USA | Sparversand (USPS Media MailTM) | Lieferung zwischen Do, 27. Jun und Mo, 1. Jul nach 43230 |
| US $19,99 (ca. EUR 18,71) | USA | Expedited Shipping | Lieferung zwischen Do, 27. Jun und Mo, 1. Jul nach 43230 |
| Bearbeitungszeit |
|---|
| Der Verkäufer verschickt den Artikel innerhalb von 3 Werktagen nach Zahlungseingang. |
| Steuern |
|---|
| Verkäufer berechnet Steuern in |
Mehrwertsteuer für diesen Artikel #166683948257
Mehrwertsteuer für diesen Artikel #166683948257
Der Verkäufer berechnet Mehrwertsteuer für Artikel, die in die folgenden Bundesstaaten verschickt werden:
| Bundesstaat | Steuersatz für Sales Tax |
|---|
Rücknahmebedingungen
| Frist | Rückerstattung erfolgt in folgender Form: | Rücksendung |
|---|---|---|
| 30 Tage | Geld zurück | Käufer zahlt Rückversand |
Der Käufer trägt die Rücksendekosten.
| Rücknahmebedingungen im Detail |
|---|
| Rücknahme akzeptiert |
Zahlungsdaten
Zahlungsmethoden
Hinweis: Bestimmte Zahlungsmethoden werden in der Kaufabwicklung nur bei hinreichender Bonität des Käufers angeboten.
Angemeldet als gewerblicher Verkäufer
Verkäuferbewertungen (11.293)
c***4 (451)- Bewertung vom Käufer.
Letzter Monat
Bestätigter Kauf
Was in great shape! Can’t wait to enjoy.
g***o (1687)- Bewertung vom Käufer.
Letzter Monat
Bestätigter Kauf
Great value great seller
a***7 (1)- Bewertung vom Käufer.
Letzter Monat
Bestätigter Kauf
Too much for shipping price
Produktbewertungen & Rezensionen
Noch mehr entdecken:
- Marguerite-Patten-Kochbücher-Sachbuch Bücher übers Kochen auf Englisch,
- Robert-A. - Heinlein-Belletristik-Bücher,
- James-A. - Michener-Belletristik-Bücher,
- Michael-A. - Singer-Sachbuch Bücher,
- Deutsche Bücher Robert-A. - Heinlein-Belletristik,
- Robert-A. - Heinlein-Taschenbuch-Belletristik-Bücher,
- Michael-A. - Singer-Taschenbuch-Sachbuch Bücher,
- Michael-A. - Singer-Taschenbuch-Lebensführung-, - Motivation- & - Karriere-Sachbuch Bücher über Selbsthilfe,
- Michael-A. - Singer-Lebensführung-, - Motivation- & - Karriere-Sachbuch Deutsche Bücher über Selbsthilfe,
- Michael-A. - Singer-Taschenbuch-Lebensführung-, - Motivation- & - Karriere-Sachbuch Deutsche Bücher über Selbsthilfe