Machine Learning

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.

Machine Learning Books

Hands-On Machine Learning with Scikit-Learn and TensorFlow

Pattern Recognition and Machine Learning (Information Science and Statistics)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

Machine Learning: A Probabilistic Perspective

Machine Learning (McGraw-Hill International Editions Computer Science Series)

Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

Information Theory, Inference, and Learning Algorithms

Artificial Intelligence: A Modern Approach

More machine learning books...

Lists

At the Interface of Algebra and Statistics by Tai-Danae Bradley

Mathematics for Human Flourishing by Francis Su

Quantum Computation and Quantum Information by Michael A. Nielsen

Categories for the Working Mathematician by Saunders Mac Lane

Category Theory in Context by Emily Riehl

At the Interface of Algebra and Statistics: Quantum Physics, Machine Learning, and Category Theory

23 books — 5 voters

The Singularity is Coming by Tony Thorne

The next big thing in tech

34 books — 31 voters

A Brief History of Artificial Intelligence by Michael Wooldridge

AI Books

8 books — 1 voter

Statistical Rethinking by Richard McElreath

Introduction to Probability by Joseph K. Blitzstein

First Course in Probability, A by Sheldon M. Ross

Statistical Science Year 1 (MCSL)

12 books — 11 voters

Reinforcement Learning by Richard S. Sutton

Information Theory, Inference, and Learning Algorithms by David J.C. MacKay

Pattern Recognition and Machine Learning by Christopher M. Bishop

Foundations of Machine Learning by Mehryar Mohri

Handbook of Practical Logic and Automated Reasoning by John E. Harrison

Machine Learning Year 3 (MCSL)

32 books — 9 voters

The Hundred-Page Machine Learning Book by Andriy Burkov

Hands-On Machine Learning with Scikit-Learn, Keras, and Tenso... by Aurélien Géron

Mathematics for Machine Learning by Marc Peter Deisenroth

Machine Learning Year 1 (MCSL)

7 books — 11 voters

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Fiction and Nonfiction Reads for Native American Heritage Month

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Quotes Tagged “Machine Learning”

“ Confabulation, or hallucination, is ubiquitous in AI/GPT output already. Efforts to correct this by self-learning algos and back propagation are unlikely to solve the problem because they add to the complexity of the system as a whole, which increases the likelihood of emergent ghosts. The difficulty is that duplicity is hard to detect unless you're a subject matter expert in the topic or you conduct your own research to test its accuracy. This begs the question — if you have to be a subject mat ...more ”
― James Rickards, MoneyGPT: AI and the Threat to the Global Economy

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