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.

Hands-On Machine Learning with Scikit-Learn and TensorFlow
Pattern Recognition and Machine Learning (Information Science and Statistics)
Deep Learning
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
The Hundred-Page Machine Learning Book
Deep Learning with Python
Python Machine Learning
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
Reinforcement Learning by Richard S. SuttonInformation Theory, Inference, and Learning Algorithms by David J.C. MacKayPattern Recognition and Machine Learning by Christopher M. BishopFoundations of Machine Learning by Mehryar MohriHandbook 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 BlumStochastic Differential Equations by Bernt ØksendalAlgebraic Geometry and Statistical Learning Theory by Sumio WatanabeProbability on Trees and Networks by Russell LyonsAlgebraic Statistics by Seth Sullivant
Statistical Science Year 4 (MCSL)
20 books — 9 voters

Human Compatible by Stuart RussellThe Hundred-Page Machine Learning Book by Andriy BurkovHands-On Machine Learning with Scikit-Learn, Keras, and Tenso... by Aurélien GéronSuperintelligence by Nick BostromMathematics for Machine Learning by Marc Peter Deisenroth
Machine Learning Year 1 (MCSL)
7 books — 11 voters
The Elements of Statistical Learning by Trevor HastieProbability and Measure by Patrick BillingsleyFoundations of Modern Probability by Olav KallenbergAn Introduction to Probability Theory and Its Applications, V... by William FellerStatistical Inference by George Casella
Statistical Science Year 3 (MCSL)
41 books — 10 voters

The Signal and the Noise by Nate SilverThe Elements of Statistical Learning by Trevor HastieMoneyball by Michael   LewisThe Visual Display of Quantitative Information by Edward R. TufteAn Introduction to Statistical Learning by Gareth James
Data Science - Learning About Data
133 books — 121 voters

James Rickards
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

A.R. Merrydew
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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