{"product_id":"probability-and-statistics-for-computer-science-9783319877884","title":"Probability and Statistics for Computer Science","description":"\u003cp\u003e • Author(s): David Forsyth\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: Mathematical \u0026amp; Statistical Software\u003c\/p\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","brand":"Springer","offers":[{"title":"Paperback","offer_id":45284769857687,"sku":"9783319877884","price":3639.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9783319877884.webp?v=1769282171","url":"https:\/\/atlanticbooks.com\/products\/probability-and-statistics-for-computer-science-9783319877884","provider":"Atlantic Books","version":"1.0","type":"link"}