Machine learning : a Bayesian and optimization perspective
By: Theodoridis, Sergios
Material type:
BookSeries: Publisher: London : Elsevier ; 2015.Description: xxi, 1050 p. : ill. ; 24 cm.ISBN: 9780128017227 (PDF ebook) :Program: ECTE363Subject(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 | T0064692 | ||
| REGULAR | University of Wollongong in Dubai Main Collection | 006.31 TH MA (Browse shelf) | Available | T0064693 |
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| 006.31 SI GN Signal processing and machine learning for brain-machine interfaces / | 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 : |
Chapter 1. Introduction Chapter 2. Probability and Stochastic Processes Chapter 3. Learning in Parametric Modeling: Basic Concepts and Directions Chapter 4: Mean-Square Error Linear Estimation Chapter 5. Stochastic Gradient Descent: The LMS Algorithm and Its Family, Chapter 6. The Least-Squares Family Chapter 7. Classification: A Tour of the Classics Chapter 8. Parameter Learning: A Convex Analytic Path Chapter 9. Sparsity-Aware Learning: Concepts and Theoretical Foundations Chapter 10. Sparsity-Aware Learning: Algorithms and Applications, Chapter 11. Learning in Reproducing Kernel Hilbert Spaces Chapter 12. Bayesian Learning: Inference and the EM Algorithm Chapter 13. Bayesian Learning: Approximate Inference and Nonparametric Models, Chapter 14. Monte Carlo Methods, Chapter 15. Probabilistic Graphical Models: Part 1, Chapter 16. Probabilistic Graphical Models: Part 2, Chapter 17. Particle Filtering Chapter 18. Neural Networks and Deep Learning, Chapter 19. Dimensionality Reduction and Latent Variables Modeling.
ECTE363