Machine Learning

A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learnin

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.Today many information source

The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduct

The subject of this textbook is deep learning, the modern incarnation of neural networks. This is the first textbook on this subject written by recognized academic author

Boosting is an approach to machine learning based on the idea of creating a highlyaccurate predictor by combining many weak and inaccurate "rules of thumb." A remarkablyrich theory has evolved around

Today's Web-enabled deluge of electronic data calls for automated methods of dataanalysis. Machine learning provides these, developing methods that can automatically detect patternsin data and then us

This graduate-level textbook introduces fundamental concepts and methods in machinelearning. It describes several important modern algorithms, provides the theoretical underpinningsof these algorithms

Boosting is an approach to machine learning based on the idea of creating a highlyaccurate predictor by combining many weak and inaccurate "rules of thumb." A remarkablyrich theory has evolved around

As the power of computing has grown over the past few decades, the field of machinelearning has advanced rapidly in both theory and practice. Machine learning methods are usuallybased on the assumptio

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in whi

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

A comprehensive introduction and reference guide to the minimum description length (MDL) Principle that is accessible to researchers dealing with inductive reference in diverse areas including statist

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past d

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the ne

The goal of machine learning is to program computers to use example data or pastexperience to solve a given problem. Many successful applications of machine learning exist already,including systems th