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Cover: 9781108940023 | Machine Learning Fundamentals: A Concise Introduction | Hui Jiang
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Machine Learning Fundamentals
A Concise Introduction
Taschenbuch von Hui Jiang
Sprache: Englisch

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Beschreibung
A coherent introduction to core concepts and deep learning techniques that are critical to academic research and real-world applications.
A coherent introduction to core concepts and deep learning techniques that are critical to academic research and real-world applications.
Über den Autor
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.
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2021
Fachbereich: Kommunikationswissenschaften
Genre: Medienwissenschaften
Rubrik: Wissenschaften
Medium: Taschenbuch
Inhalt: Kartoniert / Broschiert
ISBN-13: 9781108940023
ISBN-10: 1108940021
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Jiang, Hui
Hersteller: Cambridge University Press
Maße: 251 x 202 x 24 mm
Von/Mit: Hui Jiang
Erscheinungsdatum: 25.11.2021
Gewicht: 0,908 kg
Artikel-ID: 120259722
Über den Autor
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.
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2021
Fachbereich: Kommunikationswissenschaften
Genre: Medienwissenschaften
Rubrik: Wissenschaften
Medium: Taschenbuch
Inhalt: Kartoniert / Broschiert
ISBN-13: 9781108940023
ISBN-10: 1108940021
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Jiang, Hui
Hersteller: Cambridge University Press
Maße: 251 x 202 x 24 mm
Von/Mit: Hui Jiang
Erscheinungsdatum: 25.11.2021
Gewicht: 0,908 kg
Artikel-ID: 120259722
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