[추천도서] 인공지능 추천도서 (학부 및 대학원)

학부

실수 해석학:

- Rudin, W., Principles of Mathematical Analysis, McGraw-Hill, 3rd edition, 1976

선형대수학:

- Khan Academy, linear algebra course

- Deisenroth, Faisal, Ong, Mathematics for Machine Learning, Cambridge University Press, 2020

- Strang, Linear Algebra and Learning from Data, Wellesley-Cambridge Press, 2019

확률:

- Bertsekas and Tsitsiklis, Introduction to Probability, 2nd Ed., Athena Scientific, 2008

- Jacod, J. and P. Protter, Probability Essentials, Springer, 2nd edition, 2004

- Durrett, R., Probability: Theory and Examples, Cambridge University Press, 4.1th edition, 2013

- Grimmett, G. and D. Stirzaker,  Probability and Random Processes Theory, Oxford University Press, 3rd edition, 2001 (upper undergraduate level, measure)

최적화:

Chong, Zak, An Introduction to Optimization, 4th Ed., Wiley, 2013

대학원

실수 해석학:

- Folland, G., Real Analysis: Modern Techniques and Their Applictions, Willey, 2nd edition, 2007

- Rudin, W., Real and Complex Analysis, McGraw-Hill, 3rd edition, 1986

- Kolmogorov, A.N., Foundations of the theory of probability, Chelsea, 2nd edition, 1956