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| 008 | 171214s2018 gw |||| o |||| 0|eng | ||
| 010 | _a 2019769373 | ||
| 020 | _a9783319877884 | ||
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_a10.1007/978-3-319-64410-3 _2doi |
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| 035 | _a21938376 | ||
| 035 | _a(DE-He213)978-3-319-64410-3 | ||
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_aDLC _beng _epn _erda _cDLC |
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_a005.55 FOR 2018 _223 |
| 100 | 1 |
_aForsyth, David, _eauthor. |
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| 245 | 1 | 0 |
_aProbability and Statistics for Computer Science / _cby David Forsyth. |
| 250 | _a1st ed. 2018. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018. |
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| 300 | _a1 online resource (XXIV, 367 pages 124 illustrations, 84 illustrations in color.) | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _a1 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. | |
| 520 | _aThis 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. | ||
| 588 | _aDescription based on publisher-supplied MARC data. | ||
| 650 | 0 | _aComputer simulation. | |
| 650 | 0 | _aMathematical statistics. | |
| 650 | 0 | _aStatistics. | |
| 650 | 1 | 4 |
_aProbability and Statistics in Computer Science. _0https://scigraph.springernature.com/ontologies/product-market-codes/I17036 |
| 650 | 2 | 4 |
_aSimulation and Modeling. _0https://scigraph.springernature.com/ontologies/product-market-codes/I19000 |
| 650 | 2 | 4 |
_aStatistics and Computing/Statistics Programs. _0https://scigraph.springernature.com/ontologies/product-market-codes/S12008 |
| 776 | 0 | 8 |
_iPrint version: _tProbability and statistics for computer science _z9783319644097 _w(DLC) 2017950289 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319644097 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319644110 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319877884 |
| 906 |
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