{"product_id":"probability-and-statistics-for-computer-science-von-david-forsyth","title":"Probability and Statistics for Computer Science","description":"\n                                \n                \u003cp\u003eThis 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.\u003c\/p\u003e\n                                \n                \u003cp\u003e\n                                        With careful treatment of topics that fill the curricular needs for the course, \n                    \n                    \u003ci\u003eProbability and Statistics for Computer Science\u003c\/i\u003e\n                                         features:\n                    \n                    \u003cbr\u003e\n                                    \n                \u003c\/p\u003e\n                                \n                \u003cp\u003e\n                                        •   A treatment of random variables and expectations dealing primarily with the discrete case.\n                    \n                    \u003cbr\u003e\n                                    \n                \u003c\/p\u003e\n                                •   A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains.\n                \n                \u003cp\u003e\u003c\/p\u003e\n                                •   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.\n                \n                \u003cp\u003e\u003c\/p\u003e\n                                \n                \u003cp\u003e•   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.\u003c\/p\u003e\n                                •   A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems.\n                \n                \u003cp\u003e\u003c\/p\u003e\n                                •   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.\n                \n                \u003cp\u003e\u003c\/p\u003e\n                                \n                \u003cp\u003e \u003c\/p\u003e\n                                \n                \u003cp\u003e•   A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals.\u003c\/p\u003e\n                                \n                \u003cp\u003eIllustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as \u003c\/p\u003e\n                                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.  \n                \n                \u003cp\u003e\u003c\/p\u003e\n                                Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.\n                \n                \u003cp\u003e\u003c\/p\u003e\n                            \n            \u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783319877884\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783319644097\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e","brand":"Libri","offers":[{"title":"Softcover - 9783319877884","offer_id":39420769468509,"sku":"9783319877884","price":53.49,"currency_code":"EUR","in_stock":true},{"title":"Hardcover - 9783319644097","offer_id":1171138150406,"sku":"9783319644097","price":69.54,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/3ed01b5b-0350-46da-843d-c0454b57540e.jpg?v=1775709674","url":"https:\/\/shop.autorenwelt.de\/en\/products\/probability-and-statistics-for-computer-science-von-david-forsyth","provider":"Autorenwelt Shop","version":"1.0","type":"link"}