000			02904cam a2200313 a 4500
001			17013307
005			20140212125300.0
008			111020s2012 flua b 001 0 eng
010			_a 2011039389
020			_a9781439824146 (hardback)
040			_aDLC _cDLC _dDLC
042			_apcc
050	0	0	_aQ325.5 _b.R64 2012
082	0	0	_a006.31 ROG-GIR _223
084			_aBUS061000 _aCOM000000 _aCOM021030 _2bisacsh
100	1		_aRogers, Simon, _d1979-
245	1	2	_aA first course in machine learning _cSimon Rogers, Mark Girolami.
260			_aBoca Raton : _bCRC Press, _c2012 .
300			_axx, 285 p. : _bill. ; _c25 cm.
500			_aIncludes 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"--
650		0	_aMachine learning.
650		7	_aBUSINESS & ECONOMICS / Statistics. _2bisacsh
650		7	_aCOMPUTERS / General. _2bisacsh
650		7	_aCOMPUTERS / Database Management / Data Mining. _2bisacsh
700	1		_aGirolami, Mark, _d1963-
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
955			_bxh00 2011-10-20 _ixh07 2011-10-20 ONIX to Dewey _axn05 2012-01-23 1 copy rec'd., to CIP ver.
999			_c116658 _d116658