probability statistics computer science de forsyth david (26 résultats)

paperback. Etat : Good. Our good condition books are generally good for reading but not for gifting or collecting. They could have imperfections such as creasing, fanning, inscriptions, margin notes, yellowing, staining on edge or cover or pages, bumps, scuffs, etc etc (sometimes multiple of these). It's a wide category that encompasses anything that isn't almost-new down to anything that is slightly better than poor. We would NOT recommend gifting Good books - these should be considered reading copies. Our books are dispatched from a Yorkshire former cotton mill. We list via barcode/ISBN so please note that the images are stock images and may not be the exact copy you receive, furthermore the details about edition and year might not be accurate as many publishers reuse the same ISBN for multiple editions and as we simply scan a barcode or enter an ISBN we do not check the validity of the edition data when listing. If you're looking for an exact edition please don't order (at least not without checking with us first, although we don't always have time to check). We aim to dispatch prompty, the service used will depend on order value and book size. We can ship to most countries, see our shipping policies. Payment is via Abe only.

Etat : New.

Paperback or Softback. Etat : New. Probability and Statistics for Computer Science. Book.

Etat : New. pp. 367.

Etat : New. pp. 367.

Etat : As New. Unread book in perfect condition.

Etat : New. pp. 367.

Etat : New. In.

PF. Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Paperback. Etat : Brand New. reprint edition. 392 pages. 9.25x6.10x1.02 inches. In Stock.

Etat : New.

Taschenbuch. Etat : Neu. Neuware -This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensivebackground in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Sciencefeatures:¿ A treatment of random variables and expectations dealing primarily with the discrete case.¿ Apractical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis onMarkov chains.¿ A clear but crisp account of simple point inference strategies (maximum likelihood;Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing.¿ Achapter dealing with classification, explaining why it¿s useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methodssuch asrandom forests and nearest neighbors.¿ A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems.¿ A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis.¿ A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals.Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know.Instructor resources includea full set of model solutions forallproblems, and an Instructor's Manual with accompanying presentation slides.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 392 pp. Englisch.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science features:- A treatment of random variables and expectations dealing primarily with the discrete case.- A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains.- A clear but crisp account of simple point inference strategies (maximum likelihood; Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing.- Achapter dealing with classification, explaining why it's useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors.- A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems.- A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis. - A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals.Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know. Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science features:- A treatment of random variables and expectations dealing primarily with the discrete case.- A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains.- A clear but crisp account of simple point inference strategies (maximum likelihood; Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing.- Achapter dealing with classification, explaining why it's useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors.- A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems.- A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis. - A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals.Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know. Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.

Taschenbuch. Etat : Neu. Probability and Statistics for Computer Science | David Forsyth | Taschenbuch | xxiv | Englisch | 2019 | Springer | EAN 9783319877884 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Etat : Hervorragend. Zustand: Hervorragend | Seiten: 392 | Sprache: Englisch | Produktart: Bücher | This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning. With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science features: ¿   A treatment of random variables and expectations dealing primarily with the discrete case. ¿   A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains. ¿   A clear but crisp account of simple point inference strategies (maximum likelihood; Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing. ¿   Achapter dealing with classification, explaining why it¿s useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors.¿   A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems. ¿   A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis. ¿   A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals. Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know.   Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.

Hardcover. Etat : New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Etat : new. Questo è un articolo print on demand.

Etat : new. Questo è un articolo print on demand.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science features:- A treatment of random variables and expectations dealing primarily with the discrete case.- A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains.- A clear but crisp account of simple point inference strategies (maximum likelihood; Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing.- Achapter dealing with classification, explaining why it's useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors.- A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems.- A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis. - A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals.Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know. Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides. 392 pp. Englisch.

Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science features:- A treatment of random variables and expectations dealing primarily with the discrete case.- A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains.- A clear but crisp account of simple point inference strategies (maximum likelihood; Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing.- A chapter dealing with classification, explaining why it's useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors.- A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems.- A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis. - A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals.Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know. Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides. 392 pp. Englisch.

Etat : New. Print on Demand.

Etat : New. PRINT ON DEMAND.

Buch. Etat : Neu. Probability and Statistics for Computer Science | David Forsyth | Buch | xxiv | Englisch | 2018 | Springer | EAN 9783319644097 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.