Published Nov 3, 2017

The Computational Complexity of Machine Learning

Explore the intersection of complexity theory and machine learning with Michael Kearns as he delves into computational limits, the challenges in applying machine learning to finance, and the importance of algorithmic fairness, all while balancing accuracy and societal considerations.

Episode Highlights

Topics covered

Episode Highlights

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Dexa/Kyle Polich

The Computational Complexity of Machine Learning

Topics covered

Popular Clips

Intractable Equilibrium

Limits of Learnability

Predicting Financial Markets

Sample Complexity vs Computational Complexity

Minimizing Bias, Maximizing Accuracy

Algorithmic Fairness Debate

Nips 2017 Meetup

Balancing Complexity and Goodness of Fit

Brushing Up

Sample and Computational Complexity

Modeling Financial Markets

Advances in Machine Learning

Probabilistic Concepts

Balancing Fairness and Accuracy

Episode Highlights

Complexity in Machine Learning

Complexity in Finance

Algorithmic Fairness

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The Complexity of Learning Neural Networks

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Complexity and Cryptography

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The Computational Complexity of Machine Learning

Topics covered

Popular Clips

Episode Highlights

Complexity in Machine Learning

Complexity in Finance

Algorithmic FairnessThe discussion shifts to the importance of fairness in algorithmic decision-making, highlighting the need for precise definitions and societal input. Michael Kearns explores strategies for minimizing bias while balancing accuracy and fairness in machine learning models.

Algorithmic Fairness

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