Probability and Statistics for Computer Science

Szerző: David Forsyth

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer International Publishing AG

Elérhetőség: Beszállítói készleten

Küldés 10-13 napon belül

24 568 Ft

This undergraduate textbook is aimed at computer science undergraduates late in sophomore or early i...

Információk a könyvről

Szerző

David Forsyth

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2018

oldal

367

EAN

9783319644097

ISBN

3319644092

Enbook ID

16608756

Kiadó

Springer International Publishing AG

Súly

1122

Méretek

283 x 215 x 28

Teljes leírás

This undergraduate 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 Scientists 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 from that chapter. Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.

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Probability and Statistics for Computer Science

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