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Viser: Probability and Statistics for Computer Science

Probability and Statistics for Computer Science, 1. udgave

Probability and Statistics for Computer Science

David Forsyth
(2018)
Sprog: Engelsk
Springer
625,00 kr.
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Detaljer Om Varen

  • 1. Udgave
  • Hardback
  • Udgiver: Springer (Februar 2018)
  • ISBN: 9783319644097

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.
1 Notation and conventions 9
1.
0.
1 Background Information. 10
1.
1 Acknowledgements. 11 I Describing Datasets ; 12 2 First Tools for Looking at Data 13
2.
1 Datasets.
132.
2 What''s Happening? - Plotting Data. 15
2.
2.
1 Bar< Charts. 16
2.
2.
2 Histograms. 16
2.
2.
3 How to Make Histograms. 17
2.
2.
4 Conditional Histograms. 19
2.
3 Summarizing 1D Data. 19
2.
3.
1 The Mean. 20
2.
3.
2 Standard Deviation. 22
2.
3.
3 Computing Mean and Standard Deviation Online. 26
2.
3.
4 Variance. 26
2.
3.
5 The Median. 27
2.
3.
6 Interqu artile Range. 29
2.
3.
7 Using Summaries Sensibly. 30
2.
4 Plots and Summaries. 31
2.
4.
1 Some Properties of Histograms. 31
2.
4.
2 Standard Coordinates and Normal Data. 34
2.
4.
3 Box Plots. 38
2.
5 Whose is bigger? Inves tigating Australian Pizzas. 39
2.
6 You should. 43
2.
6.
1 remember these definitions:. 43
2.
6.
2 remember these terms. 43
2.
6.
3 remember these facts:. 43
2.
6.
4 be able to. 43 3 Looking at Relationships 47
3.
1 Plotting 2D Data. 47
3.
1.
1
3.
1.
2 Series. 51
3.
1.
3 Scatter Plots for Spatial Data. 53
3.
1.
4 Exposing Relationships with Scatter Plots. 54
3.
2 Correlation. 57
3.
2.
1 The Correlation Coefficient. 60
3.
2.
2 Using Correlation to Predict. 64
3.
2.
3 Confusion caused by co rrelation. 68 1 <3.
3 Sterile Males in Wild Horse Herds. 68
3.
4 You should. 72
3.
4.
1 remember these definitions:. 72
3.
4.
2 remember these terms. 72
3.
4.
3 remember these facts:
3.
4.
4 use these procedures:
3.
4.
5 be able to: 72 72 72 II Probability & 78 4 Basic ideas in probability &
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