Logo

Understanding Machine Learning: From Theory to Algorithms

Large book cover: Understanding Machine Learning: From Theory to Algorithms

Understanding Machine Learning: From Theory to Algorithms
by

Publisher: Cambridge University Press
ISBN/ASIN: 1107057132
ISBN-13: 9781107057135
Number of pages: 449

Description:
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.

Home page url

Download or read it online for free here:
Download link
(2.5MB, PDF)

Similar books

Book cover: Machine Learning for DesignersMachine Learning for Designers
by - O'Reilly Media
This book introduces you to contemporary machine learning systems and helps you integrate machine-learning capabilities into your user-facing designs. Patrick Hebron explains how machine-learning applications can affect the way you design websites.
(4207 views)
Book cover: Optimal and Learning Control for Autonomous RobotsOptimal and Learning Control for Autonomous Robots
by - arXiv.org
The starting point is the formulation of of an optimal control problem and deriving the different types of solutions and algorithms from there. These lecture notes aim at supporting this unified view with a unified notation wherever possible.
(3727 views)
Book cover: Machine Learning for Data StreamsMachine Learning for Data Streams
by - The MIT Press
This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA, allowing readers to try out the techniques after reading the explanations.
(3787 views)
Book cover: An Introductory Study on Time Series Modeling and ForecastingAn Introductory Study on Time Series Modeling and Forecasting
by - arXiv
This work presents a concise description of some popular time series forecasting models used in practice, with their features. We describe three important classes of time series models, viz. the stochastic, neural networks and SVM based models.
(8825 views)