Probability statistics computer science par forsyth david (25 résultats)
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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 m…achine 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.
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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.
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Etat : Sehr gut. Zustand: Sehr gut | 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 method…s, 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.
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Edité par Springer International Publishing, Springer Nature Switzerland Jun 2019, 2019
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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 meth…ods, 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.
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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, in…cluding 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.
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 392 pp. Englisch.
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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. 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, includi…ng 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 KG, Sachsenplatz 4-6, 1201 Wien 392 pp. Englisch.
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Buch. Etat : Neu. nach der Bestellung gedruckt 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, includi…ng 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.
