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Probability and Statistics for Computer Science

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Publisher:

Springer

Pages: 367
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Paperback - 9783319877884

04 June 2019

$74.99

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Hardcover - 9783319644097

20 February 2018

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Ebook - 9783319644103

13 December 2017

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This 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...

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This 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 Science 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.  

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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A?clear?but?crisp?account?of probability,?structured specifically to the needs of the undergraduate computer science student ?


Many exercises and examples using a wide range of real published datasets throughout, focusing on content that is likely to be used in practice

Easy-to-understand but careful treatment of topics, with?much emphasis?on exploratory data analysis and descriptive statistics

Topics of great practical importance (like classification, clustering, regression, and principal components analysis) covered at an undergraduate level, with an emphasis on using methods in practice on real datasets

Text broken up throughout with handy and useful sidebars which help explain the content in real time, including worked examples so that the reader can self-assess while absorbing the material

Each chapter ends with: ?
-list of definitions a student should remember,
-list of terms a student should understand,
-list of facts a student should keep in mind,
-list of procedures a student should be able to use,
-list of practical skills a student should have absorbed. ?
All end-of-chapter elements reference to their discussions within the chapter

1 Notation and conventions
2 First Tools for Looking at Data
3 Looking at Relationships
4 Basic ideas in probability
5 Random Variables and Expectations
6 Useful Probability Distributions
7 Samples and Populations
8 The Significance of Evidence
9 Experiments
10 Inferring Probability Models from Data
11 Extracting Important Relationships in High Dimensions
12 Learning to Classify
13 Clustering: Models of High Dimensional Data
14 Regression
15 Markov Chains and Hidden Markov Models
16 Resources.
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David 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. 


Professor 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.

A 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...

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David 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. 


Professor 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.

A 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.

He 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.

Among a variety of odd hobbies, he is

a compulsive diver, certified up to normoxic trimix level.

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