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
Aug 15, 2023
Distributional Reinforcement Learning
May 30, 2023
Nov 01, 2022
Introduction to Online Convex Optimization
Sep 06, 2022
Machine Learning from Weak Supervision
Aug 23, 2022
Probabilistic Machine Learning
Mar 01, 2022
Mar 30, 2021
Introduction to Machine Learning
Mar 17, 2020
Introduction to Natural Language Processing
Oct 01, 2019
Introduction to Statistical Relational Learning
Sep 22, 2019
Foundations of Machine Learning
Dec 25, 2018
Nov 13, 2018
Jun 05, 2018
Machine Learning for Data Streams
Mar 02, 2018
Nov 29, 2017
Nov 18, 2016
Introduction to Machine Learning
Aug 22, 2014
Jan 10, 2014
Aug 24, 2012
Foundations of Machine Learning
Aug 17, 2012
Machine Learning in Non-Stationary Environments
Mar 30, 2012
Jan 22, 2010
Introduction to Machine Learning
Dec 04, 2009
Probabilistic Graphical Models
Jul 31, 2009
The Minimum Description Length Principle
Mar 23, 2007
Gaussian Processes for Machine Learning
Nov 23, 2005
Introduction to Machine Learning
Oct 15, 2004
Aug 17, 2001
Jul 20, 2001
Causation, Prediction, and Search
Jan 29, 2001
Jan 20, 1999
Feb 26, 1998
Feb 24, 1998