000 02266nam a22003017a 4500
003 OSt
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008 251010b |||||||| |||| 00| 0 eng d
020 _a9781498756815
040 _beng
_cTUPM
_erda
050 _aGS Q 325.5
_bW56 2024
100 _aWinn, John Michael
245 _a Model-based machine learning/
_cby John Michael Winn
264 _aBoca Raton, FL, :
_bCRC Press,
_c2024.
300 _axvii, 450 pages :
_billustrations ;
_c24 cm
336 _2rdacontent
_atext
337 _2rdamedia
_aunmediated
338 _2rdacarrier
_avolume
500 _aCase studies
504 _aIncludes bibliographic references and index.
505 _aIntroduction. How Can Machine Learning Solve my Problem? 1. A Murder Mystery 2. Assessing People’s Skills Interlude. The Machine Learning Life Cycle 3. Meeting Your Match 4. Uncluttering Your Inbox 5. Making Recommendations 6. Understanding Asthma 7. Harnessing the Crowd 8. How to Read a Model Afterword
520 _a"Today, 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.
_bProvided by publisher
590 _aWinn, J. M. (2024). Model-based machine learning (1st ed.). CRC Press.
650 _aMachine learning
650 _aComputational models
650 _aArtificial intelligence
_xMethodology
942 _2lcc
_cBK
_n0
999 _c30854
_d30854