Machine Learning : A Bayesian and Optimization Perspective /
Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood...
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| Format: | software, multimedia |
| Language: | eng |
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London : Academic Press,
2020
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| Online Access: | https://www.sciencedirect.com/book/9780128188033 |
| _version_ | 1796764864858816512 |
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| author | Theodoridis, Sergios, author 209129 ScienceDirect (Online service) 7722 |
| author_facet | Theodoridis, Sergios, author 209129 ScienceDirect (Online service) 7722 |
| author_sort | Theodoridis, Sergios, author 209129 |
| collection | OCEAN |
| description | Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth. |
| first_indexed | 2024-03-05T17:16:06Z |
| format | software, multimedia |
| id | KOHA-OAI-TEST:603823 |
| institution | Universiti Teknologi Malaysia - OCEAN |
| language | eng |
| last_indexed | 2024-03-05T17:16:06Z |
| publishDate | 2020 |
| publisher | London : Academic Press, |
| record_format | dspace |
| spelling | KOHA-OAI-TEST:6038232023-12-06T00:49:29ZMachine Learning : A Bayesian and Optimization Perspective / Theodoridis, Sergios, author 209129 ScienceDirect (Online service) 7722 software, multimedia Electronic books 631902 London : Academic Press,2020©2020engMachine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth.Includes index.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 its 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 the 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 ModelingMachine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth.Machine learninghttps://www.sciencedirect.com/book/9780128188033URN:ISBN:9780128188033Remote access restricted to users with a valid UTM ID via VPN. |
| spellingShingle | Machine learning Theodoridis, Sergios, author 209129 ScienceDirect (Online service) 7722 Machine Learning : A Bayesian and Optimization Perspective / |
| title | Machine Learning : A Bayesian and Optimization Perspective / |
| title_full | Machine Learning : A Bayesian and Optimization Perspective / |
| title_fullStr | Machine Learning : A Bayesian and Optimization Perspective / |
| title_full_unstemmed | Machine Learning : A Bayesian and Optimization Perspective / |
| title_short | Machine Learning : A Bayesian and Optimization Perspective / |
| title_sort | machine learning a bayesian and optimization perspective |
| topic | Machine learning |
| url | https://www.sciencedirect.com/book/9780128188033 |
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