Be Charmed by These Bewitching Books from 2025

Adaptive Computation and Machine Learning Series

27 primary works • 27 total works
The MIT Press

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 lear…
Introduction To Machine Learning
by
3.77 · 249 Ratings · 18 Reviews · published 2004 · 33 editions
The goal of machine learning is to program compute…

Shelve Introduction To Machine Learning

    • Rate it:
    Bioinformatics: The Machine Learning Approach
    by
    3.55     · 49 Ratings · 3 Reviews · published 1998 · 11 editions
    A guide to machine learning approaches and their a…

    Shelve Bioinformatics: The Machine Learning Approach

    • Rate it:
    Distributional Reinforcement Learning
    by
    4.33     · 3 Ratings · 2 editions
    The first comprehensive guide to distributional re…

    Shelve Distributional Reinforcement Learning

    • Rate it:
    Machine Learning for Data Streams: With Practical Examples in MOA
    by
    3.50     · 4 Ratings · 1 edition
    A hands-on approach to tasks and techniques in dat…

    Shelve Machine Learning for Data Streams: With Practical Examples in MOA

    • Rate it:
    Semi-supervised Learning
    by
    4.29     · 14 Ratings · 1 Reviews · published 2006 · 9 editions
    A comprehensive review of an area of machine learn…

    Shelve Semi-supervised Learning

    • Rate it:
    Introduction to Natural Language Processing
    by
    4.35     · 23 Ratings · 2 Reviews · 4 editions
    A survey of computational methods for understandin…

    Shelve Introduction to Natural Language Processing

    • Rate it:
    Graphical Models for Machine Learning and Digital Communication
    by
    3.00     · 4 Ratings · published 1998 · 5 editions
    A variety of problems in machine learning and digi…

    Shelve Graphical Models for Machine Learning and Digital Communication

    • Rate it:
    Introduction to Statistical Relational Learning
    by
    3.56     · 18 Ratings · 3 Reviews · published 2007 · 6 editions
    Advanced statistical modeling and knowledge repres…

    Shelve Introduction to Statistical Relational Learning

    • Rate it:
    Deep Learning
    by
    4.44     · 2089 Ratings · 143 Reviews · published 2016 · 22 editions
    An introduction to a broad range of topics in deep…

    Shelve Deep Learning

    • Rate it:
    The Minimum Description Length Principle
    by
    4.08     · 12 Ratings · 2 Reviews · published 2007 · 5 editions
    A comprehensive introduction and reference guide t…

    Shelve The Minimum Description Length Principle

    • Rate it:
    Principles of Data Mining
    by
    3.78     · 32 Ratings · 1 Reviews · published 2001 · 10 editions
    The first truly interdisciplinary text on data min…

    Shelve Principles of Data Mining

    • Rate it:
    Introduction to Online Convex Optimization (Foundations and Trends
    by
    4.00     · 4 Ratings · 1 Reviews · 3 editions
    Introduction to Online Convex Optimization portray…

    Shelve Introduction to Online Convex Optimization (Foundations and Trends

    • Rate it:
    Learning Kernel Classifiers: Theory and Algorithms
    by
    3.60     · 10 Ratings · 1 Reviews · published 2001 · 7 editions
    An overview of the theory and application of kerne…

    Shelve Learning Kernel Classifiers: Theory and Algorithms

    • Rate it:
    Learning in Graphical Models
    by
    4.00     · 11 Ratings · published 1998 · 6 editions
    Graphical models, a marriage between probability t…

    Shelve Learning in Graphical Models

    • Rate it:
    Knowledge Graphs: Fundamentals, Techniques, and Applications
    by
    3.56     · 16 Ratings · 2 Reviews · 2 editions
    A rigorous and comprehensive textbook covering the…

    Shelve Knowledge Graphs: Fundamentals, Techniques, and Applications

    • Rate it:
    Probabilistic Graphical Models: Principles and Techniques
    by
    4.19     · 257 Ratings · 22 Reviews · published 2009 · 10 editions
    A general framework for constructing and using pro…

    Shelve Probabilistic Graphical Models: Principles and Techniques

    • Rate it:
    Foundations of Machine Learning
    by
    4.21     · 94 Ratings · 4 Reviews · 9 editions
    A new edition of a graduate-level machine learning…

    Shelve Foundations of Machine Learning

    • Rate it:
    Machine Learning: A Probabilistic Perspective
    by
    4.34     · 520 Ratings · 17 Reviews · 12 editions
    A comprehensive introduction to machine learning t…

    Shelve Machine Learning: A Probabilistic Perspective

    • Rate it:
    Probabilistic Machine Learning: An Introduction
    by
    4.53     · 53 Ratings · 3 Reviews · 4 editions
    A detailed and up-to-date introduction to machine …

    Shelve Probabilistic Machine Learning: An Introduction

    • Rate it:
    Elements of Causal Inference: Foundations and Learning Algorithms
    by
    3.63     · 49 Ratings · 5 Reviews · 4 editions
    A concise and self-contained introduction to causa…

    Shelve Elements of Causal Inference: Foundations and Learning Algorithms

    • Rate it:
    Gaussian Processes for Machine Learning
    by
    4.17     · 108 Ratings · 10 Reviews · published 2005 · 9 editions
    A comprehensive and self-contained introduction to…

    Shelve Gaussian Processes for Machine Learning

    • Rate it:
    Boosting: Foundations and Algorithms
    by
    4.00     · 31 Ratings · 2 Reviews · published 2012 · 12 editions
    An accessible introduction and essential reference…

    Shelve Boosting: Foundations and Algorithms

    • Rate it:
    Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
    by
    4.05     · 40 Ratings · 4 Reviews · published 2001 · 8 editions
    A comprehensive introduction to Support Vector Mac…

    Shelve Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

    • Rate it:
    Causation, Prediction, and Search
    by
    3.88     · 26 Ratings · 4 Reviews · published 1993 · 8 editions
    What assumptions and methods allow us to turn obse…

    Shelve Causation, Prediction, and Search

    • Rate it:
    Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach
    by
    4.00     · 1 Ratings · 2 editions
    Fundamental theory and practical algorithms of wea…

    Shelve Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach

    • Rate it:
    Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
    by
    2.67     · 3 Ratings · published 2012 · 7 editions
    Theory, algorithms, and applications of machine le…

    Shelve Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

    • Rate it:
    Reinforcement Learning: An Introduction
    by
    4.54     · 801 Ratings · 84 Reviews · 16 editions
    Richard Sutton and Andrew Barto provide a clear an…

    Shelve Reinforcement Learning: An Introduction

    • Rate it: