{"product_id":"probability-and-statistics-for-computer-science-paperback","title":"Probability and Statistics for Computer Science - Paperback","description":"\u003cp\u003eby \u003cb\u003eDavid Forsyth\u003c\/b\u003e (Author)\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- 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.\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\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003eDavid Alexander ​Forsyth is Fulton Watson Copp Chair in Computer Science at the University of Illinois at Urbana-Champaign, where he is a leading researcher in computer vision. \u003cbr\u003eProfessor Forsyth has regularly served as a program or general chair for the top conferences in computer vision, and has just finished a second term as Editor-in-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e \u003cp\u003eA Fellow of the ACM (2014) and IEEE (2009), Forsyth has also been recognized with the IEEE Computer Society's Technical Achievement Award (2005), the Marr Prize, and a prize for best paper in cognitive computer vision (ECCV 2002). Many of his former students are famous in their own right as academics or industry leaders.\u003c\/p\u003eHe is the co-author with Jean Ponce of Computer Vision: A Modern Approach (2002; 2011), published in four languages, and a leading textbook on the topic.\u003cp\u003e\u003c\/p\u003e\u003cp\u003eAmong a variety of odd hobbies, he is \u003c\/p\u003ea compulsive diver, certified up to normoxic trimix level.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 367\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.81 x 11 x 8.25 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 04, 2019\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42105687867527,"sku":"9783319877884","price":89.08,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0601\/2623\/2711\/files\/3d93141f1f87f4f09d177d546023fa07.webp?v=1732436298","url":"https:\/\/booksby.splitshops.com\/products\/probability-and-statistics-for-computer-science-paperback","provider":"Books by splitShops","version":"1.0","type":"link"}