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.