MARC details
| 000 -LEADER |
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| fixed length control field |
03603nmm a22002897a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
SPU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20210703214858.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
210519b2018 sz |||||o|||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783319644103 (E-book) |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
SPU |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA 76.9.M35 |
| Item number |
F67P 2018 |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
David A. Forsyth |
| 9 (RLIN) |
57526 |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Probability and statistics for computer science / |
| Statement of responsibility, etc. |
David Forsyth |
| Medium |
[electronic resource] |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Cham, Switzerland : |
| Name of publisher, distributor, etc. |
Springer, |
| Date of publication, distribution, etc. |
2018 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 online resource (xxiv, 367 pages) : |
| Other physical details |
illustrations (some color) |
| 449 ## - DEPARTMENT |
|---|
| Department name |
บางเขน. คณะเทคโนโลยีสารสนเทศ. เทคโนโลยีสารสนเทศ |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes index |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Notation and conventions -- First Tools for Looking at Data -- Looking at Relationships -- Basic ideas in probability -- Random Variables and Expectations -- Useful Probability Distributions -- Samples and Populations -- The Significance of Evidence -- Experiments -- Inferring Probability Models from Data -- Extracting Important Relationships in High Dimensions -- Learning to Classify -- Clustering: Models of High Dimensional Data -- Regression -- Markov Chains and Hidden Markov Models -- Resources |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Available to OhioLINK libraries |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
COMPUTER SCIENCE |
| General subdivision |
STATISTICAL METHODS |
| 9 (RLIN) |
242282 |
| 850 ## - HOLDING INSTITUTION |
|---|
| Holding institution |
SPU |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="https://drive.google.com/file/d/1MLIQCBzZKm2BhJ1g1DEcnZNo7w8kG12w/view?usp=sharing">https://drive.google.com/file/d/1MLIQCBzZKm2BhJ1g1DEcnZNo7w8kG12w/view?usp=sharing</a> |
| Link text |
View Full-text |
| 910 ## - ACQUISITION INFORMATION (SPU) |
|---|
| Purchaser |
Library |
| Publisher |
Springer |
| Accession date |
190521 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
E-Book |
| 998 ## - STAFF NAME (SPU) |
|---|
| ผู้ลงรายการ |
niparat 0521 |