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        Computers / Data Science / Machine Learning

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        Artificial Intelligence

        Artificial Intelligence

        Artificial Intelligence

        A Systems Approach from Architecture Principles to Deployment

        by David R. Martinez and Bruke M. Kifle

        ISBN: 9780262048989

        Publisher: The MIT Press

        Pub Date: June 11, 2024

        The first text to take a systems engineering approach to artificial intelligence (AI), from architecture principles to the development and deployment of AI capabilities.
        Foundations of Computer Vision

        Foundations of Computer Vision

        Foundations of Computer Vision

        by Antonio Torralba, Phillip Isola and William T. Freeman

        ISBN: 9780262048972

        Publisher: The MIT Press

        Pub Date: April 16, 2024

        An accessible, authoritative, and up-to-date computer vision textbook offering a comprehensive introduction to the foundations of the field that incorporates the latest deep learning advances.
        Discriminating Data

        Discriminating Data

        Discriminating Data

        Correlation, Neighborhoods, and the New Politics of Recognition

        by Wendy Hui Kyong Chun

        ISBN: 9780262548526

        Publisher: The MIT Press

        Pub Date: March 5, 2024

        How big data and machine learning encode discrimination and create agitated clusters of comforting rage.
        Fairness and Machine Learning

        Fairness and Machine Learning

        Fairness and Machine Learning

        Limitations and Opportunities

        by Solon Barocas, Moritz Hardt and Arvind Narayanan

        ISBN: 9780262048613

        Publisher: The MIT Press

        Pub Date: December 19, 2023

        An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning.
        Understanding Deep Learning

        Understanding Deep Learning

        Understanding Deep Learning

        by Simon J. D. Prince

        ISBN: 9780262048644

        Publisher: The MIT Press

        Pub Date: December 5, 2023

        An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.
        Code to Joy

        Code to Joy

        Code to Joy

        Why Everyone Should Learn a Little Programming

        by Michael L. Littman

        ISBN: 9780262546393

        Publisher: The MIT Press

        Pub Date: October 3, 2023

        How we can get more joy from our machines by telling them what our hearts desire.
        Probabilistic Machine Learning

        Probabilistic Machine Learning

        Probabilistic Machine Learning

        Advanced Topics

        by Kevin P. Murphy

        ISBN: 9780262048439

        Publisher: The MIT Press

        Pub Date: August 15, 2023

        An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.
        Causal Analysis

        Causal Analysis

        Causal Analysis

        Impact Evaluation and Causal Machine Learning with Applications in R

        by Martin Huber

        ISBN: 9780262545914

        Publisher: The MIT Press

        Pub Date: August 1, 2023

        A comprehensive and cutting-edge introduction to quantitative methods of causal analysis, including new trends in machine learning.
        Distributional Reinforcement Learning

        Distributional Reinforcement Learning

        Distributional Reinforcement Learning

        by Marc G. Bellemare, Will Dabney and Mark Rowland

        ISBN: 9780262048019

        Publisher: The MIT Press

        Pub Date: May 30, 2023

        The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.
        The Little Learner

        The Little Learner

        The Little Learner

        A Straight Line to Deep Learning

        by Daniel P. Friedman and Anurag Mendhekar

        Foreword by Guy L. Steele Jr. and Peter Norvig

        ISBN: 9780262546379

        Publisher: The MIT Press

        Pub Date: February 21, 2023

        A highly accessible, step-by-step introduction to deep learning, written in an engaging, question-and-answer style.
        ‹123›

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