000			02636nam a22003137a 4500
003			EG-NcFUE
005			20251204112654.0
008			251204b ua|||||| |||| 00| 0 eng d
020			_a9781498756815
040			_beng
043			_aua
082		4	_223 _a006.31 WJM
100	1		_aWinn, John Michael. _934190
245	1		_aModel-Based Machine Learning/ _cJohn Michael Winn, Christopher M. Bishop, Thomas Diethe, John Guiver, Yordan Zaykov.
250			_a1st Edition
264		1	_aBoca Raton: _bCRC Press, _c©2024.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
500			_aToday, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Features: Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. Explains machine learning concepts as they arise in real-world case studies. Shows how to diagnose, understand and address problems with machine learning systems. Full source code available, allowing models and results to be reproduced and explored. Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.
500			_aJohn Winn is a Principal Researcher at Microsoft Research, UK.
650	1	4	_aArtificial intelligence. _933122
650	1	4	_aMachine learning _vArtificial intelligence _xComputer science _934195
700	1		_aBishop, Christopher M. _934191 _eJoint Author.
700	1		_aDiethe, Thomas. _934192 _eJoint Author.
700	1		_aGuiver, John. _934193 _eJoint Author.
700	1		_aZaykov, Yordan. _934194 _eJoint Author.
942			_2ddc _cBK
999			_c13588 _d13588