공부했던것도 있고 찍먹만 해본것도 있고 걍 링크만 저장해놨던것도 있는데 걍 알아서 보셈


Wickham & Grolemund (2017) R for Data Science

https://r4ds.had.co.nz/

Burns (2012) The R Inferno

https://www.burns-stat.com/documents/books/the-r-inferno/


Durrett (2019) Probability (5th ed.)

https://services.math.duke.edu/~rtd/PTE/pte.html


Boyd & Vandenberghe (2004) Convex Optimization

https://web.stanford.edu/~boyd/cvxbook/

Bertsekas (2009) Convex Optimization Theory

https://web.mit.edu/dimitrib/www/Convex_Theory_Entire_Book.pdf

Ben-Tal, Ghaoui & Nemirovski (2006) Robust Optimization

https://sites.google.com/site/robustoptimization/

Absil, Mahoney & Sepulchre (1999) Optimization Algorithms on Matrix Manifolds

https://press.princeton.edu/absil


Efron & Hastie (2016) Computer Age Statistical Inference

https://hastie.su.domains/CASI/

James, Witten, Hastie & Tibshirani (2021) An Introduction to Statistical Learning (2nd ed.)

https://www.statlearning.com/

Hastie, Tibshirani & Friedman (2009) The Elements of Statistical Learning (2nd ed.)

https://hastie.su.domains/ElemStatLearn/


Biship (2006) Pattern Recognition and Machine Learning

https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/

Murphy (2012) Machine Learning: A Probabilistic Perspective

https://github.com/probml/pml-book

Goodfellow, Bengio & Courville (2016) Deep Learning

https://www.deeplearningbook.org/


Downey (2021) Think Bayes (2nd ed.)

https://allendowney.github.io/ThinkBayes2/

McElreath (2020) Statistical Rethinking (2nd ed.)

https://xcelab.net/rm/statistical-rethinking/

Gelman et al. (2021) Bayesian Data Analysis (3rd ed.)

http://www.stat.columbia.edu/~gelman/book/


Sutton & Barto (2018) Reinforcement Learning: An Introduction (2nd ed.)

http://incompleteideas.net/book/the-book.html

Powell (2022) Reinforcement Learning and Stochastic Optimization

https://castlelab.princeton.edu/rlso/


Pollard (1984) Convergence of Stochastic Processes

http://www.stat.yale.edu/~pollard/Books/1984book/pollard1984.pdf

Vershynin (2018) High-Dimensional Probability

https://www.math.uci.edu/~rvershyn/papers/HDP-book/HDP-book.html


Devroye et al. (1996) A Probability Theory of Pattern Recognition

https://www.szit.bme.hu/~gyorfi/pbook.pdf

Shalev-Shwartz & Ben-David (2014) Understanding Machine Learning

https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/copy.html

Mohri et al. (2018) Foundations of Machine Learning (2nd ed.)

https://cs.nyu.edu/~mohri/mlbook/

Roberts, Yaida & Hanin (2022) The Principles of Deep Learning Theory

https://deeplearningtheory.com/


MacKay (2003) Information Theory, Inference, and Learning Algorithms

https://www.inference.org.uk/mackay/itila/book.html


Rasmussen & Wiliams (2006) Gaussian Processes for Machine Learning

https://gaussianprocess.org/gpml/chapters/


Peters et al. (2017) Elements of Causal Inference

https://web.math.ku.dk/~peters/elements.html