Logo

An Introduction to Statistical Learning

Large book cover: An Introduction to Statistical Learning

An Introduction to Statistical Learning
by

Publisher: Springer
ISBN/ASIN: 1461471370
ISBN-13: 9781461471370
Number of pages: 440

Description:
This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

Home page url

Download or read it online for free here:
Download link
(8.6MB, 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.
(10235 views)
Book cover: Statistical Foundations of Machine LearningStatistical Foundations of Machine Learning
by
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. This manuscript aims to find a good balance between theory and practice.
(9657 views)
Book cover: Practical Artificial Intelligence Programming in JavaPractical Artificial Intelligence Programming in Java
by - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).
(25554 views)
Book cover: Machine Learning and Data Mining: Lecture NotesMachine Learning and Data Mining: Lecture Notes
by - University of Toronto
Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; and more.
(10485 views)