Articoli correlati a Linear Algebra and Optimization for Machine Learning:...

Linear Algebra and Optimization for Machine Learning: A Textbook - Brossura

 
9783030403461: Linear Algebra and Optimization for Machine Learning: A Textbook

Sinossi

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows:

1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts.

2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields.  Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. 

A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.


         

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. in Operations Research from the Massachusetts Institute of Technology in 1996. He has published more than 400 papers in refereed conferences and journals and has applied for or been granted more than 80 patents. He is author or editor of 19 books, including textbooks on data mining, neural networks, machine learning (for text), recommender systems, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several internal and external awards, including the EDBT Test-of-Time Award (2014), the IEEE ICDM Research Contributions Award (2015), and the ACM SIGKDD Innovation Award (2019). He has served as editor-in-chief of the ACM SIGKDD Explorations, and is currently serving as an editor-in-chief of the ACM Transactions on Knowledge Discovery from Data. He is a fellow of the SIAM, ACM, and the IEEE, for “contributions to knowledge discovery and data mining algorithms.”


          

Dalla quarta di copertina

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 

1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book fromgeneric volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts.

2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields.  Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. 

A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Compra usato

Condizioni: molto buono
Visualizza questo articolo

EUR 12,31 per la spedizione da U.S.A. a Italia

Destinazione, tempi e costi

GRATIS per la spedizione da U.S.A. a Italia

Destinazione, tempi e costi

Altre edizioni note dello stesso titolo

9783030403430: Linear Algebra and Optimization for Machine Learning: A Textbook

Edizione in evidenza

ISBN 10:  3030403432 ISBN 13:  9783030403430
Casa editrice: Springer-Nature New York Inc, 2020
Rilegato

Risultati della ricerca per Linear Algebra and Optimization for Machine Learning:...

Foto dell'editore

Aggarwal, Charu C.
Editore: Springer, 2021
ISBN 10: 3030403467 ISBN 13: 9783030403461
Antico o usato paperback

Da: Books From California, Simi Valley, CA, U.S.A.

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

paperback. Condizione: Very Good. Codice articolo mon0003668594

Contatta il venditore

Compra usato

EUR 27,28
Convertire valuta
Spese di spedizione: EUR 12,31
Da: U.S.A. a: Italia
Destinazione, tempi e costi

Quantità: 4 disponibili

Aggiungi al carrello

Foto dell'editore

Charu C. Aggarwal
Editore: Springer, 2021
ISBN 10: 3030403467 ISBN 13: 9783030403461
Nuovo Brossura

Da: Basi6 International, Irving, TX, U.S.A.

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Codice articolo ABEJUNE24-259452

Contatta il venditore

Compra nuovo

EUR 51,44
Convertire valuta
Spese di spedizione: GRATIS
Da: U.S.A. a: Italia
Destinazione, tempi e costi

Quantità: 2 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Charu C. Aggarwal
ISBN 10: 3030403467 ISBN 13: 9783030403461
Nuovo Brossura
Print on Demand

Da: moluna, Greven, Germania

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. First textbook to provide an integrated treatment of linear algebra and optimization with a special focus on machine learning issuesIncludes many examples to simplify exposition and facilitate in learning semanticallyComplemented by example. Codice articolo 466230659

Contatta il venditore

Compra nuovo

EUR 47,23
Convertire valuta
Spese di spedizione: EUR 9,70
Da: Germania a: Italia
Destinazione, tempi e costi

Quantità: Più di 20 disponibili

Aggiungi al carrello

Foto dell'editore

Aggarwal, Charu C.
Editore: Springer, 2021
ISBN 10: 3030403467 ISBN 13: 9783030403461
Nuovo Brossura

Da: California Books, Miami, FL, U.S.A.

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. Codice articolo I-9783030403461

Contatta il venditore

Compra nuovo

EUR 54,22
Convertire valuta
Spese di spedizione: EUR 7,64
Da: U.S.A. a: Italia
Destinazione, tempi e costi

Quantità: Più di 20 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Aggarwal, Charu C.
Editore: Springer 5/13/2021, 2021
ISBN 10: 3030403467 ISBN 13: 9783030403461
Nuovo Paperback or Softback

Da: BargainBookStores, Grand Rapids, MI, U.S.A.

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Paperback or Softback. Condizione: New. Linear Algebra and Optimization for Machine Learning: A Textbook 1.97. Book. Codice articolo BBS-9783030403461

Contatta il venditore

Compra nuovo

EUR 51,12
Convertire valuta
Spese di spedizione: EUR 11,46
Da: U.S.A. a: Italia
Destinazione, tempi e costi

Quantità: 5 disponibili

Aggiungi al carrello

Foto dell'editore

Charu C. Aggarwal
Editore: Springer, 2021
ISBN 10: 3030403467 ISBN 13: 9783030403461
Nuovo Brossura

Da: Books Puddle, New York, NY, U.S.A.

Valutazione del venditore 4 su 5 stelle 4 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. pp. 495. Codice articolo 26384717745

Contatta il venditore

Compra nuovo

EUR 56,68
Convertire valuta
Spese di spedizione: EUR 7,64
Da: U.S.A. a: Italia
Destinazione, tempi e costi

Quantità: 1 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Charu C. Aggarwal
ISBN 10: 3030403467 ISBN 13: 9783030403461
Nuovo Taschenbuch
Print on Demand

Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows:1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts.2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The 'parent problem' of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks.A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning. 520 pp. Englisch. Codice articolo 9783030403461

Contatta il venditore

Compra nuovo

EUR 53,49
Convertire valuta
Spese di spedizione: EUR 11,00
Da: Germania a: Italia
Destinazione, tempi e costi

Quantità: 2 disponibili

Aggiungi al carrello

Foto dell'editore

Aggarwal Charu C.
Editore: Springer, 2021
ISBN 10: 3030403467 ISBN 13: 9783030403461
Nuovo Brossura

Da: Majestic Books, Hounslow, Regno Unito

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. pp. 495. Codice articolo 379186286

Contatta il venditore

Compra nuovo

EUR 55,25
Convertire valuta
Spese di spedizione: EUR 10,25
Da: Regno Unito a: Italia
Destinazione, tempi e costi

Quantità: 1 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Aggarwal, Charu C.
Editore: Springer, 2021
ISBN 10: 3030403467 ISBN 13: 9783030403461
Nuovo Brossura

Da: GreatBookPrices, Columbia, MD, U.S.A.

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. Codice articolo 43340715-n

Contatta il venditore

Compra nuovo

EUR 48,80
Convertire valuta
Spese di spedizione: EUR 16,97
Da: U.S.A. a: Italia
Destinazione, tempi e costi

Quantità: 4 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Charu C. Aggarwal
ISBN 10: 3030403467 ISBN 13: 9783030403461
Nuovo Taschenbuch

Da: AHA-BUCH GmbH, Einbeck, Germania

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows:1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts.2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The 'parent problem' of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks.A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning. Codice articolo 9783030403461

Contatta il venditore

Compra nuovo

EUR 53,49
Convertire valuta
Spese di spedizione: EUR 14,99
Da: Germania a: Italia
Destinazione, tempi e costi

Quantità: 1 disponibili

Aggiungi al carrello

Vedi altre 10 copie di questo libro

Vedi tutti i risultati per questo libro