Textbook recommendations

101 Self-Qualification: Ready to Be a Real Scientist?

• Feynman, R. P. (2005). The pleasure of finding things out: The best short works of Richard P. Feynman. Basic Books.

Scientific writing:

• Hofmann, A. H. (2010). Scientific writing and communication: papers, proposals, and presentations. Oxford University Press.

Stochastic models:

• Ross, S. Intro to probability models.
• Ross, S. Stochastic processes.
• Karlin and Taylor. A first course to stochastic processes.
• Harchol-Balter, M. Performance Modeling and Design of Computer Systems: Queueing Theory in Action Cambridge University Press, 2013.

Probability theory:

• P. Billingsley, Probability and measures. 2nd Ed., 1999.
• P. Billingsley, Convergence of probability measures.

Matrix geometric methods:

• G. Latouche, V. Ramaswami, Introduction to matrix analytic methods in stochastic modeling. Society for Industrial and Applied Mathematics, 1987.
• M. Neuts. Matrix-geometric solutions in stochastic models: An algorithmic approach. Dover Publications, 1995.

Computer simulations:

• S. M. Ross, Simulation. 5th Edition, Academic Press, 2014
• A. M. Law and W. D. Kelton, Simulation Models and Analysis. 3nd Edition, McGraw-Hill, 2000
• S. Asmusen and P. Glynn, Stochastic Simulation, Springer, 2009
• P. Glasserman, Monte-Carlo simulation in financial engineering, Springer, 2003.

Queueing theory:

• W. Whitt. Introduction to stochastic-process limits. Springer, 2002.
• S. Asmussen. Applied probability and queues, 2nd Edition, Springer, 2003.
• H. Chen and D. Yao. Fundamentals of queueing networks. Springer, 2001.

Reinforcement learning:

• S. M. Ross, Introduction to Stochastic Dynamic Programming. Academic Press, 2014.
• D. Bertsekas, Reinforcement Learning and Optimal Control. Athena Scientific, 2019.
• R. Sutton and A. Barto, Reinforcement Learning: An Introduction. 2nd Edition, Bradford
  Books, 2018.
• C. Szepesvari, Algorithms for Reinforcement Learning. Morgan & Claypool, 2010.