Logo

Introduction To Machine Learning

Large book cover: Introduction To Machine Learning

Introduction To Machine Learning
by


Number of pages: 209

Description:
This book surveys many of the important topics in machine learning circa 1996. The intention was to pursue a middle ground between theory and practice. This book concentrates on the important ideas in machine learning -- it is neither a handbook of practice nor a compendium of theoretical proofs. The goal was to give the reader sufficient preparation to make the extensive literature on machine learning accessible.

Home page url

Download or read it online for free here:
Download link
(2.6MB, PDF)

Similar books

Book cover: Understanding Machine Learning: From Theory to AlgorithmsUnderstanding Machine Learning: From Theory to Algorithms
by - Cambridge University Press
This book introduces machine learning and the algorithmic paradigms it offers. It provides a theoretical account of the fundamentals underlying machine learning and mathematical derivations that transform these principles into practical algorithms.
(6368 views)
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.
(4694 views)
Book cover: The Elements of Statistical Learning: Data Mining, Inference, and PredictionThe Elements of Statistical Learning: Data Mining, Inference, and Prediction
by - Springer
This book brings together many of the important new ideas in learning, and explains them in a statistical framework. The authors emphasize the methods and their conceptual underpinnings rather than their theoretical properties.
(35913 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).
(18790 views)