Useful books, papers, and lectures list
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- Template (Sample)
- Deep Learning
- Statistical Learning
- Conformal Prediction
- Markov Chain Monte Carlo (MCMC)
- Bayesian Nonparametric Modeling
- Uncertainty Quantification
- Optimal Transport
- Information Theory
- Probability Theory
- Mathematical Analysis and Measure Theory
- Python and Programming
- Generative Models
The following two entries serve as templates for organizing other resources.
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Understanding Deep Learning - Simon J.D. Prince, MIT Press, 2023
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Theoretical Foundations of Conformal Prediction - Anastasios N. Angelopoulos, Rina Foygel Barber, Stephen Bates, Cambridge University Press (forthcoming), 2024
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Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 2016
- Link: [Website]
- Note: Classic textbook covering mathematical foundations (linear algebra, probability, information theory) through CNNs, RNNs, and generative models.
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Understanding Deep Learning - Simon J.D. Prince, MIT Press, 2023
- Learn PyTorch for Deep Learning: Zero to Mastery - Daniel Bourke, 2023
- Link: [Website]
- Note: Pytorch Tutorial
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The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, Jerome Friedman, Springer, 2nd Edition, 2009
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Foundations of Machine Learning - Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, MIT Press, 2nd Edition, 2018.
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Learning Theory from First Principles - Francis Bach, MIT Press, 2024.
- Theoretical Foundations of Conformal Prediction - Anastasios N. Angelopoulos, Rina Foygel Barber, Stephen Bates, Cambridge University Press (forthcoming), 2024
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A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification - Anastasios N. Angelopoulos, Stephen Bates, 2021
- Link: [arXiv]
- Note: Accessible tutorial on conformal prediction basics, split conformal, and coverage guarantees.
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Predictive Inference with the Jackknife+ - Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas, Ryan J. Tibshirani, Annals of Statistics, 2021
- Link: [arXiv]
- Note: Introduces Jackknife+ and CV+ methods for distribution-free predictive inference.
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Gaussian Processes for Machine Learning - Carl Rasmussen, Christopher Williams, MIT Press, 2006
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Bayesian Optimization - Roman Garnett, Cambridge University Press, 2023
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Information Theory: From Coding to Learning - Yury Polyanskiy, Yihong Wu, Cambridge University Press, 2023
- Link: [PDF]
- Note: Modern textbook on information theory that also covers recent applications in statistical learning theory
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Information Theory, Inference, and Learning Algorithms - David MacKay, Cambridge University Press, 2003
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Elements of Information Theory - Thomas Cover, Joy Thomas, John Wiley & Sons, Inc., 2nd Edition, 2006
- Link: [PDF]
- Note: Standard textbook on information theory
- Statistics and Information Theory - John Duchi, 2025
- Link: [PDF]
- Note: Comprehensive book on statistical applications of information theory
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Probability: Theory and Examples - Rick Durrett, Cambridge University Press, 5th Edition, 2019
- Link: [PDF]
- Note: Standard graduate textbook on measure-theoretic probability covering laws of large numbers, CLT, martingales, and Markov chains.
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High-Dimensional Probability - Roman Vershynin, Cambridge University Press (forthcoming), 2nd Edition, 2025
- Link: [Website] [PDF] [Associated Lectures]
- Note: Graduate textbook on high-dimensional random objects. Covers concentration of measure, chaining, random matrices, etc.
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Concentration inequalities: A non-asymptotic theory of independence - Stéphane Boucheron, Gábor Lugosi, Pascal Massart, Oxford University Press, 2016
- Real and Complex Analysis - Walter Rudin, McGraw-Hill, 3rd Edition, 1987
- Note: Classic "Big Rudin" covering measure theory, integration, and functional analysis foundations.
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Flow Matching Guide and Code - Yaron Lipman, Marton Havasi, Peter Holderrieth et al., 2024
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The Principles of Diffusion Models - Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon, 2025
- Mean Flows for One-step Generative Modeling - Zhengyang Geng, Mingyang Deng, Xingjian Bai, J. Zico Kolter, Kaiming He, 2025