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| An Introduction to the Philosophy of Logic |
This will be a major new resource for students working on logic, as well as for readers seeking a better understanding of philosophy of logic in its wider context.
- Presents up-to-date and accessible discussion of a foundational part of philosophical study, and of its importance for related subdisciplines
- Provides an overview of the connections between topics including metaphysics of logic, logical pluralism, and the meaning of logical constants
- Allows students to understand the relevance of philosophical issues without having to wade through complex technical jargon
Quasi-Hopf Algebras - A Categorical Approach
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| Quasi-Hopf Algebras A Categorical Approach |
More advanced readers will benefit from having recent research gathered in one place, with open questions to inspire their own research.
- Introduces beginners to the basics of quasi-Hopf algebras, including categorical machinery necessary for their study
- Contains open problems which give the reader inspiration for future research
- Brings together several advanced topics for the first time in one book
Adversarial Machine Learning
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| Adversarial Machine Learning |
Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race.
- The first book to provide a state-of-the-art review of adversarial machine learning
- Covers availability and integrity attacks, privacy-preserving mechanisms, near-optimal evasion of classifiers, and future directions for adversarial machine learning
- Includes in-depth case studies on email spam and network security
High-Dimensional Statistics - A Non-Asymptotic Viewpoint
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| High-Dimensional Statistics A Non-Asymptotic Viewpoin |
With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data.
- Almost 200 worked examples support the reader in building practical intuition and understanding the motivation for the theory
- Contains over 250 exercises - ranging in difficulty from easy to challenging - which strengthen learning, with solutions available to instructors
- The book is organized for teaching and learning, allowing instructors to choose one of several identified paths depending on course length
Enjoy your reading!
Source: Cambridge University Press
