MARC details
| 000 -LEADER |
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| fixed length control field |
03203cam a2200373 a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
CUTN |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20190906125656.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
111020s2012 flua b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781439824146 (hardback) |
| 041 ## - LANGUAGE CODE |
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| Language |
English |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
pcc |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Edition number |
23 |
| Item number |
ROG |
| 084 ## - OTHER CLASSIFICATION NUMBER |
|---|
| Classification number |
BUS061000 |
| -- |
COM000000 |
| -- |
COM021030 |
| Source of number |
bisacsh |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Rogers, Simon, |
| 245 12 - TITLE STATEMENT |
|---|
| Title |
A first course in machine learning / |
| Statement of responsibility, etc |
Simon Rogers, Mark Girolami. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Boca Raton : |
| Name of publisher, distributor, etc |
CRC Press, |
| Date of publication, distribution, etc |
2012 . |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xx, 285 p. : |
| Other physical details |
ill. ; |
| Dimensions |
25 cm. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
1.Linear Modelling: A Least Squares Approach |
| -- |
2.Linear Modelling: A Maximum Likelihood Approach |
| -- |
3.The Bayesian Approach to Machine Learning |
| -- |
4.Bayesian Inference |
| -- |
5.classification |
| -- |
6.Clustering |
| -- |
7.Principal Components Analysis and Latent Variable Models |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"Machine Learning is rapidly becoming one of the most important areas of general practice, research and development activity within Computing Sci- ence. This is re ected in the scale of the academic research area devoted to the subject and the active recruitment of Machine Learning specialists by major international banks and nancial institutions as well as companies such as Microsoft, Google, Yahoo and Amazon. This growth can be partly explained by the increase in the quantity and diversity of measurements we are able to make of the world. A particularly fascinating example arises from the wave of new biological measurement technologies that have preceded the sequencing of the first genomes. It is now possible to measure the detailed molecular state of an organism in manners that would have been hard to imagine only a short time ago. Such measurements go far beyond our understanding of these organisms and Machine Learning techniques have been heavily involved in the distillation of useful structure from them. This book is based on material taught on a Machine Learning course in the School of Computing Science at the University of Glasgow, UK. The course, presented to nal year undergraduates and taught postgraduates, is made up of 20 hour-long lectures and 10 hour-long laboratory sessions. In such a short teaching period, it is impossible to cover more than a small fraction of the material that now comes under the banner of Machine Learning. Our inten- tion when teaching this course therefore, is to present the core mathematical and statistical techniques required to understand some of the most popular Machine Learning algorithms and then present a few of these algorithms that span the main problem areas within Machine Learning: classi cation, clus- tering"-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
BUSINESS & ECONOMICS / Statistics. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
COMPUTERS / General. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
COMPUTERS / Database Management / Data Mining. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Girolami, Mark, |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
General Books |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Dates associated with a name |
1979- |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Source of heading or term |
bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Source of heading or term |
bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Source of heading or term |
bisacsh |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Dates associated with a name |
1963- |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
7 |
| b |
cbc |
| c |
orignew |
| d |
1 |
| e |
ecip |
| f |
20 |
| g |
y-gencatlg |
