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Hui Jiang, Hui (York University, Toronto) Jiang

Machine Learning Fundamentals

Name: Machine Learning Fundamentals
Author: Hui Jiang

A Concise Introduction

Gebonden Engels 2021 9781108837040

116,40

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Samenvatting

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

Specificaties

ISBN13:9781108837040

Taal:Engels

Bindwijze:Gebonden

Aantal pagina's:420

Uitgever:Cambridge University Press

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Inhoudsopgave

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.

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