Machine learning : a Bayesian and optimization perspective / Sergios Theodoridis
Material type:
TextPublication details: [Place of publication not identifed] : [publisher not identified], c2015Description: 1,075 pages : illustrations ; 24 cmISBN: - 9780128015223
- Q 325.5 .T44 2015
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National University - Manila | LRC - Graduate Studies General Circulation | Gen. Ed - CEAS | GC Q 325.5 .T44 2015 (Browse shelf(Opens below)) | c.1 | Available | NULIB000014084 |
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| GC P 98 .H36 2013 c.3 The Handbook of computational linguistics and natural language processing / | GC Q 180.55.M4 .M39 2013 Qualitative research design : an interactive approach / | GC Q 325.5 .B69 2015 Machine learning in Python : essential techniques for predictive analysis / | GC Q 325.5 .T44 2015 Machine learning : a Bayesian and optimization perspective / | GC QA 37.2 .K95 2006 Vedic mathematics / | GC QA 76.9 .D34 2015 c.1 Data science and big data analytics: discovering, analyzing, visualizing and presenting data. | GC QA 76.9 .D34 2015 c.2 Data science and big data analytics: discovering, analyzing, visualizing and presenting data. |
Includes index.
Chapter1. 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 .
This 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.
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