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        Computers / Data Science / Machine Learning

        Showing results 1-10 of 18

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        ‹12›
        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.
        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: November 28, 2023

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

        Microprediction

        Microprediction

        Building an Open AI Network

        by Peter Cotton

        ISBN: 9780262047326

        Publisher: The MIT Press

        Pub Date: November 8, 2022

        How a web-scale network of autonomous micromanagers can challenge the AI revolution and combat the high cost of quantitative business optimization.
        Learning Kernel Classifiers

        Learning Kernel Classifiers

        Learning Kernel Classifiers

        Theory and Algorithms

        by Ralf Herbrich

        ISBN: 9780262546591

        Publisher: The MIT Press

        Pub Date: November 1, 2022

        An overview of the theory and application of kernel classification methods.
        Introduction to Online Convex Optimization

        Introduction to Online Convex Optimization, second edition

        Introduction to Online Convex Optimization

        by Elad Hazan

        ISBN: 9780262046985

        Publisher: The MIT Press

        Pub Date: September 6, 2022

        In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization....
        ‹12›

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