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| #3 |  Interpretable Machine Learning Interpretable Machine Learning: A Guide For Making Black Box Models Explainable
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| #4 |  Supervised Machine Learning for Science: How to stop worrying and love your black box
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| #5 |  Statistical Rethinking: A Bayesian Course with Examples in R and Stan
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| #6 |  INFORMATION THEORY, INFERENCE, AND LEARNING ALGORITHMS.
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| #7 |  Elements of Information Theor 2nd (second) edition Text Only Elements of Information Theor 2nd (second) edition Text Only
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| #8 |  Foundations of Info-Metrics: Modeling, Inference, and Imperfect Information
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| #9 |  Advances in Info-Metrics: Information and Information Processing across Disciplines | 1 read |
| #10 |  Regression and Other Stories
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| #11 |  Bayesian Data Analysis
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| #12 |  A First Course in Causal Inference
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| #13 |  Causality Causality: Models, Reasoning and Inference
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| #14 |  Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
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| #15 |  Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science
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| #16 |  New Foundations for Information Theory: Logical Entropy and Shannon Entropy
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| #17 |  Probability Theory: The Logic of Science
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| #18 |  The Simple and Infinite Joy of Mathematical Statistics
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| #19 |  Modeling Mindsets: The Many Cultures Of Learning From Data
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