Understanding Machine Learning. From Theory to Algorithms
Par : ,Formats :
- Réservation en ligne avec paiement en magasin :
- Indisponible pour réserver et payer en magasin
- Nombre de pages397
- PrésentationRelié
- FormatGrand Format
- Poids0.903 kg
- Dimensions18,2 cm × 26,1 cm × 3,0 cm
- ISBN978-1-107-05713-5
- EAN9781107057135
- Date de parution17/07/2014
- ÉditeurCambridge University Press
Résumé
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 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 an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and nonexpert readers in statistics, computer science, mathematics, and engineering.
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 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 an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and nonexpert readers in statistics, computer science, mathematics, and engineering.