Hui Jiang (Toronto York University)

Machine Learning Fundamentals

A Concise Introduction

• Broschiertes Buch

Produktbeschreibung

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.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 418
• Erscheinungstermin: 24. März 2022
• Englisch
• Abmessung: 251mm x 202mm x 24mm
• Gewicht: 900g
• ISBN-13: 9781108940023
• ISBN-10: 1108940021
• Artikelnr.: 62110751

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.