Computers / Data Science / Machine Learning
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Learning Theory from First Principles
ISBN: 9780262049443
Publisher: The MIT Press
Pub Date: December 24, 2024
Forthcoming from the MIT Press
Multi-Agent Reinforcement Learning
Foundations and Modern Approaches
ISBN: 9780262049375
Publisher: The MIT Press
Pub Date: December 17, 2024
Forthcoming from the MIT Press
Agents in the Long Game of AI
Computational Cognitive Modeling for Trustworthy, Hybrid AI
ISBN: 9780262549424
Publisher: The MIT Press
Pub Date: October 1, 2024
A novel approach to hybrid AI aimed at developing trustworthy agent collaborators.
Artificial Intelligence
A Systems Approach from Architecture Principles to Deployment
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
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
Correlation, Neighborhoods, and the New Politics of Recognition
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
Limitations and Opportunities
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
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
Why Everyone Should Learn a Little Programming
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.
Learning Theory from First Principles
ISBN: 9780262049443
Publisher: The MIT Press
Pub Date: December 24, 2024
Forthcoming from the MIT Press
Multi-Agent Reinforcement Learning
Foundations and Modern Approaches
ISBN: 9780262049375
Publisher: The MIT Press
Pub Date: December 17, 2024
Forthcoming from the MIT Press
Agents in the Long Game of AI
Computational Cognitive Modeling for Trustworthy, Hybrid AI
ISBN: 9780262549424
Publisher: The MIT Press
Pub Date: October 1, 2024
A novel approach to hybrid AI aimed at developing trustworthy agent collaborators.
Artificial Intelligence
A Systems Approach from Architecture Principles to Deployment
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
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
Correlation, Neighborhoods, and the New Politics of Recognition
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
Limitations and Opportunities
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
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
Why Everyone Should Learn a Little Programming
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.