Foundations of Machine Learning
Open accessAbstract
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 i…
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Since there is no pre-labeling it is relatively hard to track model performance and error.
“Since there is no pre-labeling it is relatively hard to track model performance and error (Mohri et al. 2018).”
From Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response (2022) · cites this paper as Mohri et al. 2018
Machine learning methods and models enable computers to learn from data without explicit programming.
“The development of machine learning methods and models enables computers to learn from data without explicit programming (Mohri et al., 2018).”
From The Impact of Artificial Intelligence on Workers' Skills: Upskilling and Reskilling in Organisations (2023) · cites this paper as Mohri et al., 2018
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- Authors
- Mohri, M., Rostamizadeh, A., & Talwalkar, A.
- Affiliations
- New York University (US)
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 2018-11-30
- OpenAlex
- W1944672
- Cited by
- 1,122
- References
- 11
- Citations by year
- 2026: 14 · 2025: 3 · 2024: 6 · 2023: 14 · 2022: 32 · 2021: 249 · 2020: 232 · 2019: 159 · 2018: 100 · 2017: 62 · 2016: 72 · 2015: 100 · 2014: 62 · 2013: 14 · 2012: 2
- FWCI
- 64.53
- Type
- book
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The MIT Press eBooks (publishedVersion, cc-by-nc-nd)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- machine learning theory, PAC learning, generalization bounds, Rademacher complexity, VC dimension, support vector machine, kernel methods, boosting, online learning, multi-class classification, ranking, regression analysis, algorithm stability, dimensionality reduction, learning automata, reinforcement learning, model selection, maximum entropy model, Fenchel duality, concentration inequalities, information theory
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