Rudin, W., 1964. Principles of Mathematical Analysis. McGraw-Hill.
Rudin, W., 2006. Real and Complex Analysis. McGraw-Hill.
Rudin, W., 1991. Functional Analysis. McGraw-Hill.
Rockafellar, R.T., 1970. Convex Analysis. Princeton University Press.
Horn, R.A. and Johnson, C.R., 2012. Matrix Analysis. Cambridge University Press.
Billingsley, P., 2008. Probability and Measure. John Wiley & Sons.
Bollobás, B. and Béla, B., 2001. Random Graphs. Cambridge University Press.
伊藤清, 2011. 伊藤清概率论. 人民邮电出版社.
伊藤清, 2010. 随机过程. 人民邮电出版社.
Evans, L.C., 2012. An Introduction to Stochastic Differential Equations. American Mathematical Soc..
Van Loan, C.F. and Golub, G.H., 1983. Matrix Computations. Johns Hopkins University Press.
Ross, S.M., 2014. Introduction to Probability Models. Academic Press.
Levin, D.A. and Peres, Y., 2017. Markov Chains and Mixing Times. American Mathematical Soc..
Nesterov, Y., 2018. Lectures on Convex Optimization. Springer.
Boyd, S. and Vandenberghe, L., 2004. Convex Optimization. Cambridge University Press.
Nocedal, J. and Wright, S., 2006. Numerical Optimization. Springer Science & Business Media.
袁亚湘, 2008. 非线性优化计算方法. 科学出版社.
Mitzenmacher, M. and Upfal, E., 2017. Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis. Cambridge University Press.
Leiserson, C.E., Rivest, R.L., Cormen, T.H. and Stein, C., 2001. Introduction to Algorithms. MIT Press.
Tardos, E. and Kleinberg, J., 2006. Algorithm Design.
Williamson, D.P. and Shmoys, D.B., 2011. The Design of Approximation Algorithms. Cambridge University Press.
Sipser, M., 2006. Introduction to the Theory of Computation. Thomson Course Technology.
Mezard, M., Mezard, M. and Montanari, A., 2009. Information, Physics, and Computation. Oxford University Press.
Bishop, C.M., 2006. Pattern Recognition and Machine Learning. Springer.
Hastie, T., Tibshirani, R. and Friedman, J., 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.
Haykin, S., 2010. Neural Networks and Learning Machines. Pearson Education.
Murphy, K.P., 2012. Machine Learning: A Probabilistic Perspective. MIT Press.
MacKay, D.J., 2003. Information theory, Inference and Learning Algorithms. Cambridge University Press.
Shalev-Shwartz, S. and Ben-David, S., 2014. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.
Koller, D. and Friedman, N., 2009. Probabilistic Graphical Models: Principles and Techniques. MIT Press.
Cesa-Bianchi, N. and Lugosi, G., 2006. Prediction, Learning, and Games. Cambridge University Press.
Devroye, L., Györfi, L. and Lugosi, G., 2013. A Probabilistic Theory of Pattern Recognition. Springer Science & Business Media.
Sutton, R.S. and Barto, A.G., 2018. Reinforcement Learning: An Introduction. MIT Press.
Vapnik, V., 2013. The Nature of Statistical Learning Theory. Springer science & business media.
Williams, C.K. and Rasmussen, C.E., 2006. Gaussian Processes for Machine Learning. MIT Press.
Amari, S.I., 2016. Information Geometry and its Applications. Springer.
Duda, R.O., Hart, P.E. and Stork, D.G., 2012. Pattern Classification. John Wiley & Sons.
Mohri, M., Rostamizadeh, A. and Talwalkar, A., 2018. Foundations of machine learning. MIT press.
Gonzalez, R.C. and Wintz, P., 1977. Digital Image Processing. Addison-Wesley.
Bradski, G. and Kaehler, A., 2008. Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media, Inc..
Hartley, R. and Zisserman, A., 2003. Multiple View Geometry in Computer Vision. Cambridge University Press.
Forsyth, D.A. and Ponce, J., 2002. Computer Vision: A Modern Approach. Prentice Hall Professional Technical Reference.
Szeliski, R., 2010. Computer Vision: Algorithms and Applications. Springer Science & Business Media.
Marr, D., 1982. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. MIT Press.
Jurafsky, D., 2000. Speech & language processing. Pearson Education.
Schütze, H., Manning, C.D. and Raghavan, P., 2008. Introduction to Information Retrieval. Cambridge University Press.
Leskovec, J., Rajaraman, A. and Ullman, J.D., 2019. Mining of Massive Datasets. Cambridge University Press.
Croft, W.B., Metzler, D. and Strohman, T., 2010. Search Engines: Information Retrieval in Practice. Addison-Wesley.
Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P., 2007. Numerical Recipes 3rd Edition: The Art of Scientific Computing. Cambridge University Press.
Feynman, R.P., Leighton, R.B. and Sands, M., 2011. The Feynman Lectures on Physics. Basic Books.
Aigner, M., Ziegler, G.M., Hofmann, K.H. and Erdos, P., 2010. Proofs from the Book. Springer.
Kahneman, D., 2011. Thinking, Fast and Slow. Macmillan.
Pearl, J. and Mackenzie, D., 2018. The Book of Why: the New Science of Cause and Effect. Basic Books.