Paulo Rauber

Recommended reading

I recommend the following references to students interested in artificial intelligence research.

My current research is focused on developing principled but scalable Bayesian reinforcement learning methods. A typical Bayesian reinforcement learning method represents its knowledge about an environment by a probability distribution over (one-step dynamics) models and uses this knowledge to seek optimal actions. If you would like to pursue a PhD under my supervision, please skim the survey highlighted below and my selected publications.

PhD level

Bayesian reinforcement learning (notes):

• M. Ghavamzadeh, S. Mannor, J. Pineau, A. Tamar. "Bayesian Reinforcement Learning: A Survey". Foundations and Trends in Machine Learning, 2015.

Measure-theoretic probability (notes):

• D. Pollard. "A User's Guide to Measure Theoretic Probability". Cambridge University Press, 2010.

Reinforcement learning theory (notes):

• T. Lattimore, C. Szepesvári. "Bandit Algorithms". Cambridge University Press, 2020.

Artificial intelligence:

• S. Legg. "Machine Super Intelligence". PhD thesis, 2008.

MSc level

Machine learning (notes and notes):

• C. Bishop. "Pattern Recognition and Machine Learning". Springer-Verlag, 2007.
• K.P. Murphy. "Probabilistic Machine Learning: An Introduction". MIT Press, 2022.
• M.J. Kochenderfer, T.A. Wheeler, K.H. Wray. "Algorithms for Decision Making". MIT Press, 2022.

Neural networks (notes):

• S.J.D. Prince. "Understanding Deep Learning". MIT Press, 2023.

BSc level

Programming:

• E. Matthes. "Python Crash Course". No Starch Press, 2023.
• K.N. King. "C Programming: A Modern Approach". W. W. Norton & Company, 2008.

Computer design:

• D.A. Patterson, J.L. Hennessy. "Computer Organization and Design: RISC-V Edition". Morgan Kaufmann, 2020.

Mathematical proof:

• D.J. Velleman. "How to Prove It: A Structured Approach". Cambridge University Press, 2019.

Calculus (notes):

• J. Stewart. "Calculus: Early Transcendentals". Brooks/Cole, 2011.

Algorithms:

• T.H. Cormen, C.E. Leiserson, R. Rivest, C. Stein. "Introduction to Algorithms". MIT Press, 2022.

Theory of computation:

• T. Sipser. "Introduction to the Theory of Computation". Course Technology, 2012.

Analysis:

• T. Tao. "Analysis I". Springer, 2022.

Linear algebra (notes):

• S. Axler. "Linear Algebra Done Right". Springer, 1997.

Probability (notes, Sec. 2):

• D.P. Bertsekas, J.N. Tsitsiklis. "Introduction to Probability". Athena Scientific, 2008.

Artificial intelligence:

• S. Russell, P. Norvig. "Artificial Intelligence: A Modern Approach". Pearson, 2021.

Reinforcement learning (notes):

• R.S. Sutton, A.G. Barto. "Reinforcement Learning: An Introduction". MIT Press, 2020.