Zur Startseite gehen
0,00 €*
Cover: 9783030181161 | Applied Machine Learning | David Forsyth | Taschenbuch | Paperback
Dekorationsartikel gehören nicht zum Leistungsumfang.
Applied Machine Learning
Taschenbuch von David Forsyth
Sprache: Englisch

75,40 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Lieferzeit 4-7 Werktage

Kategorien:
Beschreibung
Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren¿t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one¿s own code.
A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).
Emphasizing the usefulness ofstandard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:
¿ classification using standard machinery (naive bayes; nearest neighbor; SVM)
¿ clustering and vector quantization (largely as in PSCS)
¿ PCA (largely as in PSCS)
¿ variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)
¿ linear regression (largely as in PSCS)
¿ generalized linear models including logistic regression
¿ model selection with Lasso, elasticnet
¿ robustness and m-estimators
¿ Markov chains and HMM¿s (largely as in PSCS)
¿ EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they¿ve been through that, the next one is easy
¿ simple graphical models (in the variational inference section)
¿ classification with neural networks, with a particular emphasis on
image classification
¿ autoencoding with neural networks
¿ structure learning
Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren¿t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one¿s own code.
A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).
Emphasizing the usefulness ofstandard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:
¿ classification using standard machinery (naive bayes; nearest neighbor; SVM)
¿ clustering and vector quantization (largely as in PSCS)
¿ PCA (largely as in PSCS)
¿ variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)
¿ linear regression (largely as in PSCS)
¿ generalized linear models including logistic regression
¿ model selection with Lasso, elasticnet
¿ robustness and m-estimators
¿ Markov chains and HMM¿s (largely as in PSCS)
¿ EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they¿ve been through that, the next one is easy
¿ simple graphical models (in the variational inference section)
¿ classification with neural networks, with a particular emphasis on
image classification
¿ autoencoding with neural networks
¿ structure learning
Über den Autor
David Forsyth grew up in Cape Town. He received a B.Sc. (Elec. Eng.) from the University of the Witwatersrand, Johannesburg in 1984, an M.Sc. (Elec. Eng.) from that university in 1986, and a D.Phil. from Balliol College, Oxford in 1989. He spent three years on the faculty at the University of Iowa, ten years on the faculty at the University of California at Berkeley, and then moved to the University of Illinois. He served as program co-chair for IEEE Computer Vision and Pattern Recognition in 2000, 2011, 2018 and 2021; general co-chair for CVPR 2006 and ICCV 2019, and program co-chair for the European Conference on Computer Vision 2008, and is a regular member of the program committee of all major international conferences on computer vision. He has served six terms on the SIGGRAPH program committee. In 2006, he received an IEEE technical achievement award, in 2009 he was named an IEEE Fellow, and in 2014 he was named an ACM Fellow. He served as Editor-in-Chief of IEEE TPAMI from 2014-2017. He is lead co-author of Computer Vision: A Modern Approach, a textbook of computer vision that ran to two editions and four languages. He is sole author of Probability and Statistics for Computer Science, which provides the background for this book. Among a variety of odd hobbies, he is a compulsive diver, certi¿ed up to normoxic trimix level.
Zusammenfassung

Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career

Broad coverage of the area ensures enough to get the reader started, and to realize that it's worth knowing more in-depth knowledge of the topic

Practical approach emphasizes using existing tools and packages quickly, with enough pragmatic material on deep networks to get the learner started without needing to study other material

Inhaltsverzeichnis
1. Learning to Classify.- 2. SVM's and Random Forests.- 3. A Little Learning Theory.- 4. High-dimensional Data.- 5. Principal Component Analysis.- 6. Low Rank Approximations.- 7. Canonical Correlation Analysis.- 8. Clustering.- 9. Clustering using Probability Models.- 10. Regression.- 11. Regression: Choosing and Managing Models.- 12. Boosting.- 13. Hidden Markov Models.- 14. Learning Sequence Models Discriminatively.- 15. Mean Field Inference.- 16. Simple Neural Networks.- 17. Simple Image Classi¿ers.- 18. Classifying Images and Detecting Objects.- 19. Small Codes for Big Signals.- Index.
Details
Erscheinungsjahr: 2020
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 516
Inhalt: xxi
494 S.
73 s/w Illustr.
86 farbige Illustr.
494 p. 159 illus.
86 illus. in color.
ISBN-13: 9783030181161
ISBN-10: 3030181162
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Forsyth, David
Auflage: 1st ed. 2019
Hersteller: Springer International Publishing
Maße: 254 x 178 x 28 mm
Von/Mit: David Forsyth
Erscheinungsdatum: 14.08.2020
Gewicht: 0,96 kg
preigu-id: 118817125
Über den Autor
David Forsyth grew up in Cape Town. He received a B.Sc. (Elec. Eng.) from the University of the Witwatersrand, Johannesburg in 1984, an M.Sc. (Elec. Eng.) from that university in 1986, and a D.Phil. from Balliol College, Oxford in 1989. He spent three years on the faculty at the University of Iowa, ten years on the faculty at the University of California at Berkeley, and then moved to the University of Illinois. He served as program co-chair for IEEE Computer Vision and Pattern Recognition in 2000, 2011, 2018 and 2021; general co-chair for CVPR 2006 and ICCV 2019, and program co-chair for the European Conference on Computer Vision 2008, and is a regular member of the program committee of all major international conferences on computer vision. He has served six terms on the SIGGRAPH program committee. In 2006, he received an IEEE technical achievement award, in 2009 he was named an IEEE Fellow, and in 2014 he was named an ACM Fellow. He served as Editor-in-Chief of IEEE TPAMI from 2014-2017. He is lead co-author of Computer Vision: A Modern Approach, a textbook of computer vision that ran to two editions and four languages. He is sole author of Probability and Statistics for Computer Science, which provides the background for this book. Among a variety of odd hobbies, he is a compulsive diver, certi¿ed up to normoxic trimix level.
Zusammenfassung

Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career

Broad coverage of the area ensures enough to get the reader started, and to realize that it's worth knowing more in-depth knowledge of the topic

Practical approach emphasizes using existing tools and packages quickly, with enough pragmatic material on deep networks to get the learner started without needing to study other material

Inhaltsverzeichnis
1. Learning to Classify.- 2. SVM's and Random Forests.- 3. A Little Learning Theory.- 4. High-dimensional Data.- 5. Principal Component Analysis.- 6. Low Rank Approximations.- 7. Canonical Correlation Analysis.- 8. Clustering.- 9. Clustering using Probability Models.- 10. Regression.- 11. Regression: Choosing and Managing Models.- 12. Boosting.- 13. Hidden Markov Models.- 14. Learning Sequence Models Discriminatively.- 15. Mean Field Inference.- 16. Simple Neural Networks.- 17. Simple Image Classi¿ers.- 18. Classifying Images and Detecting Objects.- 19. Small Codes for Big Signals.- Index.
Details
Erscheinungsjahr: 2020
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 516
Inhalt: xxi
494 S.
73 s/w Illustr.
86 farbige Illustr.
494 p. 159 illus.
86 illus. in color.
ISBN-13: 9783030181161
ISBN-10: 3030181162
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Forsyth, David
Auflage: 1st ed. 2019
Hersteller: Springer International Publishing
Maße: 254 x 178 x 28 mm
Von/Mit: David Forsyth
Erscheinungsdatum: 14.08.2020
Gewicht: 0,96 kg
preigu-id: 118817125
Warnhinweis

Ähnliche Produkte

Ähnliche Produkte

Buch
Cover: 9780387989556 | Probability via Expectation | Peter Whittle | Buch | xxi | Englisch
Probability via Expectation
EAN: 9780387989556
Sprache: Englisch
Von/Mit: Peter Whittle

120,95 €*

Lieferzeit 2-3 Wochen

Taschenbuch
Cover: 9783801723026 | Statistik - Testverfahren | Heinz Holling (u. a.) | Taschenbuch | 2015
Statistik - Testverfahren
EAN: 9783801723026
Sprache: Deutsch
Von/Mit: Heinz Holling (u. a.)

29,95 €*

Lieferzeit 1-2 Werktage

Buch
Cover: 9780471901556 | Clinical Trials | Pocock | Buch | HC gerader Rücken kaschiert | XII
Clinical Trials
A Practical Approach
EAN: 9780471901556
Sprache: Englisch
Von/Mit: Stuart J Pocock

111,50 €*

Aktuell nicht verfügbar

Taschenbuch
Cover: 9783658289546 | Statistik | Einführung mit praktischen Beispielen | Josef Puhani
Statistik
Einführung mit praktischen Beispielen
EAN: 9783658289546
Sprache: Deutsch
Von/Mit: Josef Puhani

29,99 €*

Lieferzeit 1-2 Werktage

Taschenbuch
Cover: 9781617296284 | Designing APIs with Swagger and OpenAPI | Joshua Ponelat (u. a.)
Designing APIs with Swagger and OpenAPI
EAN: 9781617296284
Sprache: Englisch
Von/Mit: Joshua Ponelat (u. a.)

69,15 €*

Lieferzeit 1-2 Wochen

Taschenbuch
Cover: 9781780175690 | Business Analysis Techniques | 123 essential tools for success | Buch
Business Analysis Techniques
123 essential tools for success
EAN: 9781780175690
Sprache: Englisch
Von/Mit: James Cadle (u. a.)

66,10 €*

Lieferzeit 1-2 Wochen

Taschenbuch
Cover: 9780134845623 | Machine Learning with Python for Everyone | Mark Fenner | Taschenbuch
Machine Learning with Python for Everyone
EAN: 9780134845623
Sprache: Englisch
Von/Mit: Mark Fenner

59,25 €*

Lieferzeit 1-2 Wochen

Taschenbuch
Cover: 9781529115925 | Plowing the Dark | Richard Powers | Taschenbuch | Englisch | 2021
Plowing the Dark
EAN: 9781529115925
Sprache: Englisch
Von/Mit: Richard Powers

16,35 €*

Lieferzeit 1-2 Wochen

Buch
Cover: 9783347998537 | Künstliche Intelligenz und wie sie die Geschichte der Menschheit...
Künstliche Intelligenz und wie sie die Geschichte der Menschheit verändert
Von den Anfängen der KI bis hin zu ChatGPT | Auswirkungen, Gefahren, Chancen und wie auch Sie von der KI profitieren...
EAN: 9783347998537
Sprache: Deutsch
Von/Mit: B. Centik

21,99 €*

Lieferzeit 2-3 Wochen

Taschenbuch
Cover: 9781785420856 | AI Art | Machine Visions and Warped Dreams | Joanna Zylinska | Buch
AI Art
Machine Visions and Warped Dreams
EAN: 9781785420856
Sprache: Englisch
Von/Mit: Joanna Zylinska

49,95 €*

Lieferzeit 1-2 Werktage

Diese Website verwendet Cookies, um eine bestmögliche Erfahrung bieten zu können. Mehr Informationen ...