Understanding Machine Learning: From Theory To Algorithms
Hebrew University of Jerusalem · University of Waterloo
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 an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic…
Citation impact
- FWCI
- 99.39
- Percentile
- 100%
- References
- 192
Authors
2Topics & keywords
- Computer science
- Artificial intelligence
- Field (mathematics)
- Machine learning
- Computational learning theory
- Stability (learning theory)
- Algorithmic learning theory
- Stochastic gradient descent
- Quality Education