Probability And Statistics For Computer Science - Forsyth David | Libro Springer 06/2019 - HOEPLI.it


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Probability and Statistics for Computer Science




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Dettagli

Genere:Libro
Lingua: Inglese
Editore:

Springer

Pubblicazione: 06/2019
Edizione: Softcover reprint of the original 1st ed. 2018





Trama

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.





Sommario

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....................................................................................

................................... 13

2.2        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                                                                                  &

nbsp;    78

4    Basic ideas in probability                                                                                        79

4.1        Experiments, Outcomes and Probability....................................................... 79

4.1.1          Outcomes and Probability...................................................................... 79

4.2        Events.................

.......................................................................................................... 81

4.2.1          Computing Event Probabilities by Counting Outcomes............. 83

4.2.2          The Probability of Events...................................................................... 87

4.2.3          Computing Probabilities by Reasoning about Sets...................... 89

4.3        Independence............................................................................................................ 92

4.3.1          Example: Airline Overbooking............................................................ 96

4.4        Conditional ...........................................

............. 99

4.4.1          Evaluating Conditional Probabilities.............................................. 100

4.4.2          Detecting Rare Events is Hard......................................................... 104

4.4.3          Conditional Probability and Various Forms of Independence . 106 4.4.4 The Prosecutor’s Fallacy                108

4.4.5     Example: The Monty Hall Problem................................................ 110

4.5        Extra Worked Examples.................................................................................... 112

4.5.1          Outcomes and Probability........................................

........................... 112

4.5.2          Events...............................................................................





Autore

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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Altre Informazioni

ISBN:

9783319877884

Condizione: Nuovo
Dimensioni: 279 x 210 mm Ø 990 gr
Formato: Brossura
Illustration Notes:40 Illustrations, black and white
Pagine Arabe: 367
Pagine Romane: xxiv






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