1 Sách Về Học Máy (Machine Learning)

1.1 Sách nhập môn học máy

[IML.1] Christopher Bishop. Pattern Recognition and Machine Learning.

[IML.2] Kevin P. Murphy. Machine Learning: A Probabilistic Perspective.

[IML.3] Andrew Barto, Richard S. Sutton. Reinforcement Learning: An Introduction.

[IML.4] Daniela Witten, Gareth M. James, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning: With Applications in R.

[IML.5] Aaron Courville, Ian Goodfellow, Yoshua Bengio. Deep Learning.

[IML.6] Christopher Bishop. Neural Networks for Pattern Recognition.

[IML.7] Nir Friedman, Daphne Koller. Probabilistic Graphical Models: Principles and Techniques.

1.2 Sách học máy nâng cao

[AML.1] Trevor Hastie, Robert Tibshirani, Jerome H. Friedman. The Elements of Statistical Learning.

[AML.2] Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning.

[AML.3] Carl Edward Rasmussen, Christopher K. I. Williams. Gaussian Processes for Machine Learning.

[AML.4] Vladimir Vapnik. The Nature of Statistical Learning Theory.

[AML.5] Shai Ben-David, Shai Shalev-Shwartz. Understanding Machine Learning: From Theory to Algorithms.

[AML.6] Martin J. Wainwright and Michael I. Jordan. Graphical models, exponential families, and variational inference.

[AML.7] Gabriel Peyré, Marco Cuturi. Computational Optimal Transport.

[AML.8] Raman Arora, Sanjeev Arora, Joan Bruna, Nadav Cohen, Simon Du, Rong Ge, Suriya Gunasekar, Chi Jin, Jason Lee, Tengyu Ma, Behnam Neyshabur, Zhao Song. Theory of Deep Learning.

[AML.9] Alekh Agarwal, Nan Jiang, Sham M. Kakade, Wen Sun. Reinforcement Learning: Theory and Algorithms.

2 Sách Về Xác Suất và Thống Kê (Probability and Statistics)

2.1 Sách nhập môn thống kê

[IS.1] Larry A. Wasserman. All of Statistics: A Concise Course in Statistical Inference.

[IS.2] Larry A. Wasserman. All of Nonparametric Statistics.

[IS.3] George Casella, Roger Lee Berger. Statistical Inference.

[IS.4] Aad van der Vaart. Asymptotic Statistics.

[IS.5] Andrew Gelman, Donald Rubin, Aki Vehtari, John Carlin, Hal S. Stern, David Dunson. Bayesian Data Analysis.

[IS.6] Fernando Andres Quintana, Alejandro Jara, Tim Hanson, Peter Muller. Bayesian Nonparametric Data Analysis.

[IS.7] Bradley Efron, Robert Tibshirani. An Introduction to the Bootstrap.

[IS.8] Christian P Robert, George Casella. Introducing Monte Carlo Methods with R.

2.2 Sách nhập môn xác suất

[IP.1] Sheldon M. Ross. A First Course In Probability.

[IP.2] Y.A. Rozanov. Probability Theory: A Concise Course.

2.3 Sách thống kê nâng cao

[AS.1] Stephane Boucheron, Gabor Lugosi, Pascal Massart. Concentration Inequalities: A Nonasymptotic Theory of Independence.

[AS.2] Roman Vershynin. High dimensional probability. An introduction with applications in Data Science.

[AS.3] Martin J. Wainwright. High-dimensional statistics: A non-asymptotic viewpoint.

[AS.4] Trevor Hastie, Robert Tibshirani and Martin J. Wainwright. Statistical Learning with Sparsity: the Lasso and Generalizations.

[AS.5] Aad van der Vaart, Subhashis Ghosal. Fundamentals of Nonparametric Bayesian Inference.

[AS.6] Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker. Bayesian Nonparametrics.

[AS.7] Alexandre B. Tsybakov. Introduction to Nonparametric Estimation.

2.4 Sách xác suất nâng cao

[AP.1] Rick Durrett. Probability: Theory and Examples.

[AP.2] Kai-lai Chung. A Course in Probability Theory.

[AP.3] Cédric Villani. Topics in Optimal Transportation.

[AP.4] Cédric Villani. Optimal Transport: Old and New

[AP.5] David Asher Levin, Yuval Peres, Elizabeth Wilmer. Markov Chains and Mixing Times.

3 Sách Về Tối Ưu (Optimization

3.1 Sách nhập môn tối ưu

[IO.1] Lieven Vandenberghe, Stephen P. Boyd. Convex Optimization.

3.2 Sách tối ưu nâng cao

[AO.1] Suvrit Sra, Stephen J. Wright, Sebastian Nowozin. Optimization for Machine Learning.

[AO.2] Dimitri Bertsekas. Convex Optimization Algorithms.

[AO.3] Sébastien Bubeck. Convex Optimization: Algorithms and Complexity.

[AO.4] Stephen J. Wright, J. Nocedal. Numerical Optimization.