語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Probability and Statistics for Compu...
~
SpringerLink (Online service)
Probability and Statistics for Computer Science
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Probability and Statistics for Computer Science/ by David Forsyth.
作者:
Forsyth, David.
面頁冊數:
XXIV, 367 p. 124 illus., 84 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Mathematical statistics. -
電子資源:
https://doi.org/10.1007/978-3-319-64410-3
ISBN:
9783319644103
Probability and Statistics for Computer Science
Forsyth, David.
Probability and Statistics for Computer Science
[electronic resource] /by David Forsyth. - 1st ed. 2018. - XXIV, 367 p. 124 illus., 84 illus. in color.online resource.
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.
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.
ISBN: 9783319644103
Standard No.: 10.1007/978-3-319-64410-3doiSubjects--Topical Terms:
527941
Mathematical statistics.
LC Class. No.: QA276-280
Dewey Class. No.: 005.55
Probability and Statistics for Computer Science
LDR
:04100nam a22004095i 4500
001
996938
003
DE-He213
005
20200705133207.0
007
cr nn 008mamaa
008
201225s2018 gw | s |||| 0|eng d
020
$a
9783319644103
$9
978-3-319-64410-3
024
7
$a
10.1007/978-3-319-64410-3
$2
doi
035
$a
978-3-319-64410-3
050
4
$a
QA276-280
072
7
$a
UYAM
$2
bicssc
072
7
$a
COM077000
$2
bisacsh
072
7
$a
UYAM
$2
thema
072
7
$a
UFM
$2
thema
082
0 4
$a
005.55
$2
23
100
1
$a
Forsyth, David.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
643465
245
1 0
$a
Probability and Statistics for Computer Science
$h
[electronic resource] /
$c
by David Forsyth.
250
$a
1st ed. 2018.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2018.
300
$a
XXIV, 367 p. 124 illus., 84 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
505
0
$a
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.
520
$a
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.
650
0
$a
Mathematical statistics.
$3
527941
650
0
$a
Computer simulation.
$3
560190
650
0
$a
Statistics .
$3
1253516
650
1 4
$a
Probability and Statistics in Computer Science.
$3
669886
650
2 4
$a
Simulation and Modeling.
$3
669249
650
2 4
$a
Statistics and Computing/Statistics Programs.
$3
669775
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783319644097
776
0 8
$i
Printed edition:
$z
9783319644110
776
0 8
$i
Printed edition:
$z
9783319877884
856
4 0
$u
https://doi.org/10.1007/978-3-319-64410-3
912
$a
ZDB-2-SCS
912
$a
ZDB-2-SXCS
950
$a
Computer Science (SpringerNature-11645)
950
$a
Computer Science (R0) (SpringerNature-43710)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入