東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

A first course in machine learning /

Rogers, Simon, (1979-)

FindBook

Google Book

Amazon

博客來

A first course in machine learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A first course in machine learning // Simon Rogers, Mark Girolami.
作者:	Rogers, Simon,
其他作者:	Girolami, Mark,
出版者:	Boca Raton :CRC Press, : 2012 .,
面頁冊數:	xx, 285 p. :ill. ;25 cm.
標題:	Machine learning. -
ISBN:	9781439824146 (hbk.) :

A first course in machine learning /
Rogers, Simon,1979-

A first course in machine learning /Simon Rogers, Mark Girolami. - Boca Raton :CRC Press,2012 . - xx, 285 p. :ill. ;25 cm. - Chapman & Hall/CRC machine learning & pattern recognition series. - Chapman & Hall/CRC machine learning & pattern recognition series..

Includes bibliographical references and index.

"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"--

ISBN: 9781439824146 (hbk.) :UK34.99Subjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .R64 2012

Dewey Class. No.: 006.3/1

A first course in machine learning /
LDR:02463cam a2200205 a 4500 001 1267303
003 OCoLC
005 20120904212403.0
008 120907s2012 flua b 001 0 eng d
020 $a 9781439824146 (hbk.) : $c UK34.99
020 $a 1439824142 (hbk.)
035 $a AS-BW-101-04
040 $a DLC $b eng
050 0 0 $a Q325.5 $b .R64 2012
082 0 0 $a 006.3/1 $2 23
100 1 $a Rogers, Simon, $d 1979- $3 1535370
245 1 0 $a A first course in machine learning / $c Simon Rogers, Mark Girolami.
260 # $a Boca Raton : $b CRC Press, $c 2012 .
300 $a xx, 285 p. : $b ill. ; $c 25 cm.
490 1 0 $a Chapman & Hall/CRC machine learning & pattern recognition series
504 $a Includes bibliographical references and index.
520 # $a "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"-- $c Provided by publisher.
650 # 0 $a Machine learning. $3 533906
700 1 # $a Girolami, Mark, $d 1963- $3 1072653
830 0 $a Chapman & Hall/CRC machine learning & pattern recognition series. $3 1535367