000			01984nam a2200229Ia 4500
003			NULRC
005			20250520102827.0
008			250520s9999 xx 000 0 und d
020			_a9780128015223
040			_cNULRC
050			_aQ 325.5 .T44 2015
100			_aTheoridis, Sergios. _eauthor
245		0	_aMachine learning : _ba Bayesian and optimization perspective / _cSergios Theodoridis
260			_a[Place of publication not identifed] : _b[publisher not identified], _cc2015
300			_a1,075 pages : _billustrations ; _c24 cm.
365			_bUSD180.91
504			_aIncludes index.
505			_aChapter1. Probability and stochastic processes -- Chapter2. Learning in parametric modeling: basic concepts and directions -- Chapter3. Mean-square error linear estimation -- Chapter4. Stochastic gradient descent: the LMS algorithm -- Chapter5. The least-squares family -- Chapter6. Classification: a tour of the classics -- Chapter7. Parameter learning: a convex analytic path -- Chapter8. Sparsity-aware learning: concepts and theoretical foundations -- Chapter9. Sparcity-aware learning: algorithms and applications -- Chapter10. Learning in reproducing Kernel Hilbert spaces -- Chapter11. Bayesian learning: inference and the EM algorithm -- Chapter12. Bayesian learning: approximate inference and nonparametric models -- Chapter13. Monte Carlo methods -- Chapter14. Probabilistic graphical models: Part I -- Chapter15. Probabilistic graphical models: Part II -- Chapter16. Particle filtering -- Chapter17. Neural networks and deep learning -- Chapter18. Dimensionality reduction and Latent Variables Modeling .
520			_aThis tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches--which are based on optimization techniques--together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models.
650			_aMACHINE LEARNING
942			_2lcc _cBK
999			_c16325 _d16325