The Art of Computer Programming, Volume 1: Fundamental Algorithms (3rd Edition) eBook
- Delivery: Can be download immediately after purchasing. For new customer, we need process for verification from 30 mins to 24 hours.
- Version: PDF/EPUB. If you need another version, please Contact us
- Quality: Full page, full content, high quality images, searchable text and you can print it.
- Compatible Devices: Can be read on any devices (Kindle, NOOK, Android/IOS devices, Windows, MAC,..).
- e-Book Features: Purchase and read your book immediately, access your eTextbook anytime and anywhere, unlimited download and share with friends.
- Note: If you do not receive the download link within 15 minutes of your purchase, please Contact us. Thank you!
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 Learningis 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.