Learning Theory from First Principles
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- Nombre de pages475
- PrésentationRelié
- FormatGrand Format
- Poids1.115 kg
- Dimensions18,5 cm × 23,5 cm × 3,0 cm
- ISBN978-0-262-04944-3
- EAN9780262049443
- Date de parution20/12/2024
- CollectionAdaptive Computation and Machi
- ÉditeurMIT Press (The)
Résumé
Provides a balanced and unified treatment of most prevalent machine learning methods. Covers modern topics not found in existing texts, such as overparameterized models and structured prediction ; Integrates coverage of statistical theory, optimization theory, and approximation theory ; Focuses on adaptivity, allowing distinctions between various learning techniques ; Features hands-on experiments, illustrative examples, and accompanying code Francis Bach is a researcher at Inria, where he leads the machine learning team, which is part of the Computer Science Department at Ecole Normale Supérieure.
His research focuses on machine learning and optimization.
Provides a balanced and unified treatment of most prevalent machine learning methods. Covers modern topics not found in existing texts, such as overparameterized models and structured prediction ; Integrates coverage of statistical theory, optimization theory, and approximation theory ; Focuses on adaptivity, allowing distinctions between various learning techniques ; Features hands-on experiments, illustrative examples, and accompanying code Francis Bach is a researcher at Inria, where he leads the machine learning team, which is part of the Computer Science Department at Ecole Normale Supérieure.
His research focuses on machine learning and optimization.