Autor: David Forsyth
ISBN-13: 9783319644097
Veröffentl: 01.02.2018
Einband: Book
Seiten: 367
Gewicht: 1129 g
Format: 284x212x30 mm
Sprache: Englisch

Probability and Statistics for Computer Science

 Book
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99
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 N 182

6.4.1 Large N..............................................................
......................................... 183

6.4.2 Getting Normal 2 Tests of Model Fit............................................................................ 239

8.4 Dangerous Behavior............................................................................................. 244

8.5 You should............................................................................................................... 246

8.5.1 remember these definitions:......................................
........................ 246

8.5.2 remember

8.5.3

remember these facts: . .

. . .

8.5.4

use these procedures: . . .

. . .

8.5.5

be able to: . . . . . . . . .
. . .

. . . . . . . . . . . . . . . . . 246

. . . . . . . . . . . . . . . . . 246

. . . . . . . . . . . . . . . . . 246

9 Experiments &nbs

p; 251

9.1 A Simple Experiment: The Effect of a Treatment.................................. 251

9.1.1 Randomized Balanced Experiments............................................... 252
9.1.2 Decomposing Error in Predictions.................................................. 253

9.1.3 Estimating the Noise Variance......................................................... 253

9.1.4 The ANOVA Table.................................................................................. 255

9.1.5 Unbalanced Experiments.................................................................... 257

9.1.6 Significant Differences.......................................................................... 259

9.2 Two Factor Experiments.................................................................................... 261

9.2.1 &n
bsp; Decomposing the Error........................................................................ 264

9.2.2 Interaction Between Effects................................................................ 265

9.2.3 The Effects of a Treatment................................................................. 266
9.2.4 Setting up an ANOVA Table.............................................................. 267

9.3 You should............................................................................................................... 272

9.3.1 remember these definitions:.............................................................. 272

9.3.2
remember these terms......................................................................... 272

9.3.3 remember these facts:........................................................................... 272

9.3.4 use these procedures............................................................................. 272

9.3.5 be able to.................................................................................................... 272

9.3.6 Two-Way Experiments.......................................................................... 274

10 Inferring Probability Models from Data &n
bsp; 275

10.1 Estimating Model Parameters with Maximum Likelihood.................. 275

10.1.1 The Maximum Likelihood Principle............................................... 277

10.1.2 Binomial, Geometric and Multinomial Distributions................ 278

10.1.3 Poisson and Normal Distributions................................................... 281
10.1.4 Confidence
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.
Autor: David Forsyth
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.

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Autor: David Forsyth
ISBN-13:: 9783319644097
ISBN: 3319644092
Erscheinungsjahr: 01.02.2018
Verlag: Springer-Verlag GmbH
Gewicht: 1129g
Seiten: 367
Sprache: Englisch
Sonstiges: Buch, 284x212x30 mm, Bibliographie