Machine Learning Fundamentals
A Concise Introduction
(Sprache: Englisch)
This lucid and coherent introduction to supervised machine learning presents core concepts in a concise, logical and easy-to-follow way for readers with some mathematical preparation but no prior exposure to machine learning. Coverage includes widely used...
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This lucid and coherent introduction to supervised machine learning presents core concepts in a concise, logical and easy-to-follow way for readers with some mathematical preparation but no prior exposure to machine learning. Coverage includes widely used traditional methods plus recently popular deep learning methods.
Inhaltsverzeichnis zu „Machine Learning Fundamentals “
1. Introduction; 2. Mathematical Foundation; 3. Supervised Machine Learning (in a nutshell); 4. Feature Extraction; 5. Statistical Learning Theory; 6. Linear Models; 7. Learning Discriminative Models in General; 8. Neural Networks; 9. Ensemble Learning; 10. Overview of Generative Models; 11. Unimodal Models; 12. Mixture Models; 13. Entangled Models; 14. Bayesian Learning; 15. Graphical Models.
Autoren-Porträt von Hui Jiang
York University, Toronto
Bibliographische Angaben
- Autor: Hui Jiang
- 2022, XVIII, 373 Seiten, Maße: 20,2 x 25,1 cm, Taschenbuch, Englisch
- Verlag: Cambridge University Pr.
- ISBN-10: 1108940021
- ISBN-13: 9781108940023
- Erscheinungsdatum: 31.01.2022
Sprache:
Englisch
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