| 000 -LEADER |
|---|
| fixed length control field |
03073cam a2200325 a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20230906145237.0 |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
AEF |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.3/1 |
| Edition number |
23rd ed. |
| Item number |
DAU |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
DaumeIII, Hal. |
| 9 (RLIN) |
3166 |
| 245 12 - TITLE STATEMENT |
|---|
| Title |
A first course in machine learning / |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
: |
| Name of publisher, distributor, etc. |
, |
| Date of publication, distribution, etc. |
2013. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
221 p. : |
| Other physical details |
ill. ; |
| Dimensions |
25 cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references and index. |
| 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"-- |
| Assigning source |
Provided by publisher. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Machine learning. |
| 9 (RLIN) |
3167 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
BUSINESS & ECONOMICS / Statistics. |
| Source of heading or term |
bisacsh |
| 9 (RLIN) |
3168 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
COMPUTERS / General. |
| Source of heading or term |
bisacsh |
| 9 (RLIN) |
3169 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
COMPUTERS / Database Management / Data Mining. |
| Source of heading or term |
bisacsh |
| 9 (RLIN) |
3170 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Book |
