Logo

Statistical Foundations of Machine Learning

Small book cover: Statistical Foundations of Machine Learning

Statistical Foundations of Machine Learning
by


Number of pages: 269

Description:
This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. In particular, we focus on supervised learning problems, where the goal is to model the relation between a set of input variables, and one or more output variables, which are considered to be dependent on the inputs in some manner.

Download or read it online for free here:
Download link
(7MB, PDF)

Similar books

Book cover: Reinforcement Learning and Optimal ControlReinforcement Learning and Optimal Control
by - Athena Scientific
The book considers large and challenging multistage decision problems, which can be solved by dynamic programming and optimal control, but their exact solution is computationally intractable. We discuss solution methods that rely on approximations.
(9814 views)
Book cover: Introduction to Machine LearningIntroduction to Machine Learning
by - arXiv
Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
(22618 views)
Book cover: Introduction to Machine Learning for the SciencesIntroduction to Machine Learning for the Sciences
by - arXiv.org
This is an introductory machine learning course specifically developed with STEM students in mind, written by the theoretical Condensed Matter Theory group at the University of Zurich. We discuss supervised, unsupervised, and reinforcement learning.
(3312 views)
Book cover: An Introduction to Statistical LearningAn Introduction to Statistical Learning
by - Springer
This book provides an introduction to statistical learning methods. It contains a number of R labs with detailed explanations on how to implement the various methods in real life settings and it is a valuable resource for a practicing data scientist.
(10138 views)