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

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

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

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

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

Information Theory, Inference, and Learning Algorithms

Artificial Intelligence: A Modern Approach

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Lists

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

Foundations of Data Science by Avrim Blum

Stochastic Differential Equations by Bernt Øksendal

Algebraic Geometry and Statistical Learning Theory by Sumio Watanabe

Probability on Trees and Networks by Russell Lyons

Statistical Science Year 4 (MCSL)

20 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

The Elements of Statistical Learning by Trevor Hastie

Probability and Measure by Patrick Billingsley

Foundations of Modern Probability by Olav Kallenberg

An Introduction to Probability Theory and Its Applications, V... by William Feller

Statistical Science Year 3 (MCSL)

41 books — 10 voters

More than a Glitch by Meredith Broussard

Weapons of Math Destruction by Cathy O'Neil

Invisible Women by Caroline Criado Pérez

Automating Inequality by Virginia Eubanks

Becoming An Ethical Data Scientist

36 books — 9 voters

The Visual Display of Quantitative Information by Edward R. Tufte

An Introduction to Statistical Learning by Gareth James

Data Science - Learning About Data

133 books — 121 voters

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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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“ The power of one man’s imagination is infinite. The disinterest of the human race in facing the obvious, is exponentially far greater. ”
― A.R. Merrydew

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