{"product_id":"9783319644097","title":"Probability and Statistics for Computer Science","description":"\u003ch1\u003eProbability and Statistics for Computer Science\u003c\/h1\u003e \u003ch2\u003eForsyth, David\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\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\u003cp\u003eWith careful treatment of topics that fill the curricular needs for the course, \u003ci\u003eProbability and Statistics for Computer Science\u003c\/i\u003e features:\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e•   A treatment of random variables and expectations dealing primarily with the discrete case.\u003cbr\u003e\u003c\/p\u003e•   A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains.\u003cp\u003e\u003c\/p\u003e•   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.\u003cp\u003e\u003c\/p\u003e\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•   A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems.\u003cp\u003e\u003c\/p\u003e•   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.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\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\u003cp\u003eIllustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as \u003c\/p\u003eboxed 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.  \u003cp\u003e\u003c\/p\u003eInstructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.\u003cp\u003e\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2018-02-20\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9783319644097\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-319-64410-3\u003c\/p\u003e \u003cp\u003eDimensions: 279cm x210cm\u003c\/p\u003e \u003cp\u003ePages: 367\u003c\/p\u003e ","brand":"Springer International Publishing","offers":[{"title":"Default Title","offer_id":45228279103628,"sku":"9783319644097","price":62.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783319644097_a98b7f85-727f-4ccf-9a2b-9eca8edfb208.jpg?v=1776776585","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783319644097","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}