Introduction to machine learning
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task ca...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Alpaydin, Ethem [verfasserIn] |
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| Format: |
E-Book |
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| Sprache: |
Englisch |
| Erschienen: |
Cambridge, Massachusetts: MIT Press ; 2014 |
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| Ausgabe: |
Third edition. |
| Schlagwörter: | |
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| Schlagwörter: |
| Anmerkung: |
Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 |
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| Umfang: |
1 PDF (640 pages). |
| Beschreibung: |
Mode of access: World Wide Web. |
| Reihe: |
Adaptive computation and machine learning series |
|---|
| Links: | |
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| ISBN: |
978-0-262-32574-5 |
| Katalog-ID: |
1727348079 |
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| 520 | |a The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. | ||
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9780262325745 electronic 978-0-262-32574-5 (DE-627)1727348079 (DE-599)KEP055122507 (OCoLC)894732347 (ZBM)1298.68002 (MITPRESS)6895440 (EBP)055122507 DE-627 ger DE-627 rda eng *68-01 msc 68T05 msc Alpaydin, Ethem verfasserin aut Introduction to machine learning Ethem Alpaydin Third edition. Cambridge, Massachusetts MIT Press [2014] 1 PDF (640 pages). Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. Mode of access: World Wide Web. Machine learning s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd (DE-627) 9780262028189 Erscheint auch als Druck-Ausgabe 9780262028189 https://ieeexplore.ieee.org/book/6895440 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:1298.68002 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2014 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 045F 006.3/1 22 01 0018 3848474166 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957239672 olr-MIT i z 23-07-21 62 01 0028 3742913069 OLR-MIT Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 26-08-20 100 01 3100 4472466783 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221066 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740748621 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6895440 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6895440 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6895440 62 01 0028 https://ieeexplore.ieee.org/book/6895440 100 01 3100 https://ieeexplore.ieee.org/book/6895440 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6895440 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6895440 2015 01 DE-93 https://ieeexplore.ieee.org/book/6895440 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 62 01 0028 OLR-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
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9780262325745 electronic 978-0-262-32574-5 (DE-627)1727348079 (DE-599)KEP055122507 (OCoLC)894732347 (ZBM)1298.68002 (MITPRESS)6895440 (EBP)055122507 DE-627 ger DE-627 rda eng *68-01 msc 68T05 msc Alpaydin, Ethem verfasserin aut Introduction to machine learning Ethem Alpaydin Third edition. Cambridge, Massachusetts MIT Press [2014] 1 PDF (640 pages). Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. Mode of access: World Wide Web. Machine learning s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd (DE-627) 9780262028189 Erscheint auch als Druck-Ausgabe 9780262028189 https://ieeexplore.ieee.org/book/6895440 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:1298.68002 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2014 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 045F 006.3/1 22 01 0018 3848474166 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957239672 olr-MIT i z 23-07-21 62 01 0028 3742913069 OLR-MIT Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 26-08-20 100 01 3100 4472466783 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221066 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740748621 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6895440 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6895440 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6895440 62 01 0028 https://ieeexplore.ieee.org/book/6895440 100 01 3100 https://ieeexplore.ieee.org/book/6895440 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6895440 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6895440 2015 01 DE-93 https://ieeexplore.ieee.org/book/6895440 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 62 01 0028 OLR-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
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9780262325745 electronic 978-0-262-32574-5 (DE-627)1727348079 (DE-599)KEP055122507 (OCoLC)894732347 (ZBM)1298.68002 (MITPRESS)6895440 (EBP)055122507 DE-627 ger DE-627 rda eng *68-01 msc 68T05 msc Alpaydin, Ethem verfasserin aut Introduction to machine learning Ethem Alpaydin Third edition. Cambridge, Massachusetts MIT Press [2014] 1 PDF (640 pages). Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. Mode of access: World Wide Web. Machine learning s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd (DE-627) 9780262028189 Erscheint auch als Druck-Ausgabe 9780262028189 https://ieeexplore.ieee.org/book/6895440 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:1298.68002 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2014 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 045F 006.3/1 22 01 0018 3848474166 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957239672 olr-MIT i z 23-07-21 62 01 0028 3742913069 OLR-MIT Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 26-08-20 100 01 3100 4472466783 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221066 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740748621 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6895440 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6895440 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6895440 62 01 0028 https://ieeexplore.ieee.org/book/6895440 100 01 3100 https://ieeexplore.ieee.org/book/6895440 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6895440 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6895440 2015 01 DE-93 https://ieeexplore.ieee.org/book/6895440 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 62 01 0028 OLR-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
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9780262325745 electronic 978-0-262-32574-5 (DE-627)1727348079 (DE-599)KEP055122507 (OCoLC)894732347 (ZBM)1298.68002 (MITPRESS)6895440 (EBP)055122507 DE-627 ger DE-627 rda eng *68-01 msc 68T05 msc Alpaydin, Ethem verfasserin aut Introduction to machine learning Ethem Alpaydin Third edition. Cambridge, Massachusetts MIT Press [2014] 1 PDF (640 pages). Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. Mode of access: World Wide Web. Machine learning s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd (DE-627) 9780262028189 Erscheint auch als Druck-Ausgabe 9780262028189 https://ieeexplore.ieee.org/book/6895440 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:1298.68002 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2014 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 045F 006.3/1 22 01 0018 3848474166 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957239672 olr-MIT i z 23-07-21 62 01 0028 3742913069 OLR-MIT Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 26-08-20 100 01 3100 4472466783 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221066 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740748621 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6895440 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6895440 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6895440 62 01 0028 https://ieeexplore.ieee.org/book/6895440 100 01 3100 https://ieeexplore.ieee.org/book/6895440 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6895440 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6895440 2015 01 DE-93 https://ieeexplore.ieee.org/book/6895440 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 62 01 0028 OLR-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
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