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Books & Textbooks

Topics in this section

adoption, AI in medicine, causal inference, data science, economics, epistemology, explainability, ML fundamentals, ML interpretability, philosophy, public/private interface, reinforcement learning, technology, 2010s


Prediction Machines
by Ajay Agrawal, Avi Goldfarb, and Joshua Gans (Economics of AI)

Superintelligence: Paths, Dangers, Strategies
by Nick Bostrom (Philosophy and epistemology of AI)

Race After Technology: Abolitionist Tools for the New Jim Code
by Ruha Benjamin (AI ethics)

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence by Kate Crawford (AI ethics)

Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
by Virginia Eubanks (AI ethics)

Introduction to Data Science: Data Analysis & Prediction Algorithms with R
by Rafael A. Irizarry

The Ethical Algorithm: The Science of Socially Aware Algorithm Design
by Michael Kearns and Aaron Roth (AI ethics)

Genius Makers
by Cade Metz (AI's rapid adoption in the 2010s)

Algorithms of Oppression: How Search Engines Reinforce Racism
by Safiya Noble (AI ethics)

Weapons of Math Destruction
by Cathy O'Neil (AI ethics)

Deep Medicine
by Eric Topol (AI in Medicine)

The Age of Surveillance Capitalism
by Shoshana Zuboff  (Economics of AI, ethics of AI, technology and the public/private interface)


The Hundred-Page Machine Learning Book
by Andriy Burkov (ML fundamentals)

Oxford Handbook on the Ethics of AI
by Markus Dubber, Sunit Das, Frank Pasquale (AI ethics)

Mathematics for Machine Learning
by A. Aldo Faisal, Cheng Soon Ong, Marc Peter Deisenroth (ML fundamentals)

Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow
by Aurélien Géron (ML fundamentals, statistical learning, deep learning)

Deep Learning
by Ian Goodfellow, Yoshua Bengio, Aaron Courville (Deep learning)

Data Science from Scratch: First Principles with Python
by Joel Grus (Data science, ML fundamentals)

Elements of Statistical Learning
by Trevor Hastie, Robert Tibshirani, Jerome Friedman (ML fundamentals, statistical learning)

Causal Inference: What If 
by Miguel A. Hernán and James M. Robins (Causal Inference)

Introduction to Statistical Learning
by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (ML fundamentals, statistical learning)

Interpretable Machine Learning
by Christoph Molnar (ML interpretability, explainability)

Artificial Intelligence: A Modern Approach
by Stuart Russell, Peter Norvig (AI)

Reinforcement Learning: An Introduction
by Richard S. Sutton, Andrew G. Barto (Reinforcement Learning)