Understanding Machine Learning
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Abstract
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
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The record
- Venue
- Cambridge University Press eBooks
- Topic
- Neural Networks and Applications
- Field
- Computer Science
- Canadian institutions
- University of Waterloo
- Funders
- —
- Keywords
- Computer scienceArtificial intelligenceStability (learning theory)Algorithmic learning theoryComputational learning theoryMachine learningPresentation (obstetrics)Stochastic gradient descentConvexityOnline machine learningArtificial neural network
- Has abstract in OpenAlex
- yes