04380nam a2200409 a 4500 20260701202233.0 m d cr cnu---uuuuu 171214s2018 s 000 0 eng d 9783319644103 978-3-319-64410-3 CO-CtgCURN spa coctgcurn 005.55 23 Forsyth, David., autor. aut. http://id.loc.gov/vocabulary/relators/aut Probability and Statistics for Computer Science [electronic resource] / by David Forsyth. Cham : : : Springer International Publishing : : : Imprint: Springer,,, 2018. 2018. XXIV, 367 p. 124 illus., 84 illus. in color. : online resource. texto txt rdacontent computador c rdamedia recurso en línea cr rdacarrier text file PDF rda Incluye referencias bibliográficas e índice. 1 Notation and conventions -- 2 First Tools for Looking at Data -- 3 Looking at Relationships -- 4 Basic ideas in probability -- 5 Random Variables and Expectations -- 6 Useful Probability Distributions -- 7 Samples and Populations -- 8 The Significance of Evidence -- 9 Experiments -- 10 Inferring Probability Models from Data -- 11 Extracting Important Relationships in High Dimensions -- 12 Learning to Classify -- 13 Clustering: Models of High Dimensional Data -- 14 Regression -- 15 Markov Chains and Hidden Markov Models -- 16 Resources. 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. Ciencias de la computación. Simulación en computadores. Estadística matemática. Probabilidad en ciencias de la computación. Simulación y modelación. SpringerLink (Online service) Springer eBooks MHC MHC CF 005.55 F735 <img src="/screens/gifs/go4.gif" alt="Go button" border="0" width="21" height="21" hspace="7" align=middle"> Vea este libro electrónico CF 005.55 F735 ddc a2850992 29625 29625 0 0 ddc 0 005_550000000000000_F735 0 CF MHC MHC CF 2025-03-29 0 005.55 F735 2025-03-29 17:09:44 CF 2025-03-29