Learning Theory from First Principles
Grand Format
74,40 €
Dernière sortie
Learning Theory from First Principles
This comprehensive text presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures. Francis Bach focuses on the theoretical analysis of learning algorithms as it relates to their practical performance. Accessible and engaging, this book offers the simplest formulations that can be derived from first principles, constructing mathematically rigorous results and proofs without overwhelming students.
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.
This comprehensive text presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures. Francis Bach focuses on the theoretical analysis of learning algorithms as it relates to their practical performance. Accessible and engaging, this book offers the simplest formulations that can be derived from first principles, constructing mathematically rigorous results and proofs without overwhelming students.
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.
