Machine learning : a Bayesian and optimization perspective
By: Theodoridis, Sergios
Material type:
BookPublisher: United Kingdom : Academic press ; 2020.Edition: Second edition.Description: 1 online resource : illustrations (colour).ISBN: 9780128188040 (ePub ebook) :Subject(s): Machine learning -- Mathematical modelsDDC classification: 006.31 TH MA Online resources: Location Map
| Item type | Home library | Call number | Status | Notes | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
| REGULAR | University of Wollongong in Dubai Main Collection | 006.31 TH MA (Browse shelf) | Available | June2020 | T0064695 |
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| 006.31 TH MA Machine learning for absolute beginners : | 006.31 TH MA Machine learning : | 006.31 TH MA Machine learning : | 006.31 TH MA Machine learning : | 006.31 WA MA Machine learning refined : | 006.31 ZH FE Feature engineering for machine learning : | 006.310151 LI MA Machine learning for signal processing : |
Previous edition: London: Academic Press, 2015.
1. Introduction 2. Probability and stochastic Processes 3. Learning in parametric Modeling: Basic Concepts and Directions 4. Mean-Square Error Linear Estimation 5. Stochastic Gradient Descent: the LMS Algorithm and it's Family 6. The Least-Squares Family 7. Classification: A Tour of the Classics 8. Parameter Learning: A Convex Analytic Path 9. Sparsity-Aware Learning: Concepts and Theoretical Foundations 10. Sparsity-Aware Learning: Algorithms and Applications 11. Learning in Reproducing Kernel Hilbert Spaces 12. Bayesian Learning: Inference and EM Algorithm 13. Bayesian Learning: Approximate Inference and nonparametric Models 14. Montel Carlo Methods 15. Probabilistic Graphical Models: Part 1, 16. Probabilistic Graphical Models: Part 2, 17. Particle Filtering 18. Neural Networks and Deep Learning 19. Dimensionality Reduction and Latent Variables Modeling