Probability and Statistics for Computer Science

David Forsyth (Autor)

Buch | Softcover

XXIV, 367 Seiten

2019 | 1. Softcover reprint of the original 1st ed. 2018
Springer International Publishing (Verlag)
978-3-319-87788-4 (ISBN)

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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.

- 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.

- 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.

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.

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.

Erscheinungsdatum	06.03.2019
Zusatzinfo	XXIV, 367 p. 124 illus., 84 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	210 x 279 mm
Gewicht	951 g
Themenwelt	Mathematik / Informatik ► Informatik ► Theorie / Studium
	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
	Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra
	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
Schlagworte	Boxplots • Clustering • Conditional probability • confidence intervals • continuous distributions • DataSets • discrete distributions • Expected values • markov chains • random variables • Regression • Significance of evidence • Spatial Data • Summarizing 1D data
ISBN-10	3-319-87788-7 / 3319877887
ISBN-13	978-3-319-87788-4 / 9783319877884
Zustand	Neuware