Tekijä: Shai Shalev-Shwartz; Shai Ben-David Kustantaja: Cambridge University Press (2014) Saatavuus: | Arvioimme, että tuote lähetetään meiltä noin 1-3 viikossa
Tekijä: Shai Ben David; John Case; Akira Maruoka Kustantaja: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG (2004) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Shai Ben- David; Giuseppe Curigliano; David Koff; Barbara Alicja Jereczek-Fossa; Davide La Torre; Gabriella Pravettoni Kustantaja: Elsevier Science Publishing Co Inc (2024) Saatavuus: | Arvioimme, että tuote lähetetään meiltä noin 1-3 viikossa
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.
