|
|
Product Description
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.
This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review.
This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Customers Who Bought This Item Also Bought
- High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics)
- High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics)
- Understanding Machine Learning: From Theory to Algorithms
- Reinforcement Learning and Optimal Control
- Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
- Introduction to Deep Learning (The MIT Press)
- Pattern Recognition and Machine Learning (Information Science and Statistics)
- Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
- Neural Networks and Deep Learning: A Textbook
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
*If this is not the "Foundations of Machine Learning (Adaptive Computation and Machine Learning series)" product you were looking for, you can check the other results by clicking this link
.
Details were last updated on May 11, 2024 14:51 +08.
