Hui Jiang

Machine Learning Fundamentals (eBook, PDF)

A Concise Introduction

• Format: PDF

Produktbeschreibung

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 "e;from scratch"e; 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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 31. Oktober 2021
• Englisch
• ISBN-13: 9781108945530
• Artikelnr.: 66182600

Autorenporträt

Hui Jiang is Professor of Electrical Engineering and Computer Science at York University, where he has been since 2002. His main research interests include machine learning, particularly deep learning, and its applications to speech and audio processing, natural language processing, and computer vision. Over the past 30 years, he has worked on a wide range of research problems from these areas and published hundreds of technical articles and papers in the mainstream journals and top-tier conferences. His works have won the prestigious IEEE Best Paper Award and the ACL Outstanding Paper honor.

Inhaltsangabe

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.