Adaptive Computation and Machine Learning series

The goal of building systems that can adapt to their environments and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuroscience, and cognitive science. Out of this research has come a wide variety of learning techniques, including methods for learning decision trees, decision rules, neural networks, statistical classifiers, and probabilistic graphical models. The researchers in these various areas have also produced several different theoretical frameworks for understanding these methods, such as computational learning theory, Bayesian learning theory, classical statistical theory, minimum description length theory, and statistical mechanics approaches. These theories provide insight into experimental results and help to guide the development of improved learning algorithms. A goal of the series is to promote the unification of the many diverse strands of machine learning research and to foster high quality research and innovative applications. This series will publish works of the highest quality that advance the understanding and practical application of machine learning and adaptive computation. Research monographs, introductory and advanced level textbooks, how-to books for practitioners will all be considered. For information on the submission of proposals and manuscripts, please contact any of the series editors above or the publisher, Elizabeth Swayze (epswayze@mit.edu). The series editor is Francis Bach.

Series editor: Francis Bach

Probabilistic Machine Learning

Kevin P. Murphy

Aug 15, 2023

Distributional Reinforcement Learning

Marc G. Bellemare, Will Dabney, Mark Rowland

May 30, 2023

Learning Kernel Classifiers

Ralf Herbrich

Nov 01, 2022

Machine Learning from Weak Supervision

Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai, Gang Niu

Aug 23, 2022

Probabilistic Machine Learning

Kevin P. Murphy

Mar 01, 2022

Knowledge Graphs

Mayank Kejriwal, Craig A. Knoblock, Pedro Szekely

Mar 30, 2021

Introduction to Machine Learning

Ethem Alpaydın

Mar 17, 2020

Introduction to Statistical Relational Learning

Lise Getoor, Ben Taskar

Sep 22, 2019

Foundations of Machine Learning

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

Dec 25, 2018

Reinforcement Learning

Richard S. Sutton, Andrew G. Barto

Nov 13, 2018

Learning with Kernels

Bernhard Schölkopf, Alexander J. Smola

Jun 05, 2018

Machine Learning for Data Streams

Albert Bifet, Ricard Gavaldà, Geoffrey Holmes, Bernhard Pfahringer

Mar 02, 2018

Elements of Causal Inference

Jonas Peters, Dominik Janzing, Bernhard Schölkopf

Nov 29, 2017

Deep Learning

Ian Goodfellow, Yoshua Bengio, Aaron Courville

Nov 18, 2016

Introduction to Machine Learning

Ethem Alpaydın

Aug 22, 2014

Boosting

Robert E. Schapire, Yoav Freund

Jan 10, 2014

Machine Learning

Kevin P. Murphy

Aug 24, 2012

Foundations of Machine Learning

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

Aug 17, 2012

Machine Learning in Non-Stationary Environments

Masashi Sugiyama, Motoaki Kawanabe

Mar 30, 2012

Semi-Supervised Learning

Olivier Chapelle, Bernhard Schölkopf, Alexander Zien

Jan 22, 2010

Introduction to Machine Learning

Ethem Alpaydın

Dec 04, 2009

Probabilistic Graphical Models

Daphne Koller, Nir Friedman

Jul 31, 2009

The Minimum Description Length Principle

Peter D. Grünwald

Mar 23, 2007

Gaussian Processes for Machine Learning

Carl Edward Rasmussen, Christopher K. I. Williams

Nov 23, 2005

Introduction to Machine Learning

Ethem Alpaydın

Oct 15, 2004

Principles of Data Mining

David J. Hand, Heikki Mannila, Padhraic Smyth

Aug 17, 2001

Bioinformatics

Pierre Baldi, Søren Brunak

Jul 20, 2001

Causation, Prediction, and Search

Peter Spirtes, Clark Glymour, Richard Scheines

Jan 29, 2001

Learning in Graphical Models

Michael I. Jordan

Jan 20, 1999

Reinforcement Learning

Richard S. Sutton, Andrew G. Barto

Feb 26, 1998

Bioinformatics

Pierre Baldi, Søren Brunak

Feb 24, 1998