Paulo Rauber

Recommended reading

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

I am currently focused on unlocking the potential of formalization to accelerate the development of machine learning theory by contributing to Lean Machine Learning. If you would like to work under my supervision, you should be interested in at least some of the references on machine learning theory listed below.

Undergraduate level

• Programming:
  • E. Matthes. "Python Crash Course". No Starch Press, 2023.
  • K.N. King. "C Programming: A Modern Approach". W. W. Norton & Company, 2008.
  • G. Hutton. "Programming in Haskell". Cambridge University Press, 2016.
• 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.
  • D.J. Velleman. "How to Prove It with Lean". Available here.
• 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:
  • M. 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. [Note: Second edition.]
• 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.

Graduate level

• Supervised learning and unsupervised learning (notes and notes):
  • C. Bishop. "Pattern Recognition and Machine Learning". Springer-Verlag, 2006.
• Neural networks (notes):
  • S.J.D. Prince. "Understanding Deep Learning". MIT Press, 2023.
• Reinforcement learning and decision making (notes):
  • R.S. Sutton, A.G. Barto. "Reinforcement Learning: An Introduction". MIT Press, 2018.
  • S. Levine. "Deep Reinforcement Learning". Available here.
  • M.J. Kochenderfer, T.A. Wheeler, K.H. Wray. "Algorithms for Decision Making". MIT Press, 2022.
• Measure-theoretic probability (notes):
  • D. Pollard. "A User's Guide to Measure Theoretic Probability". Cambridge University Press, 2010.
• Machine learning theory (notes and notes):
  • T. Lattimore, C. Szepesvári. "Bandit Algorithms". Cambridge University Press, 2020.
  • A. Agarwal, N. Jiang, S.M. Kakade, W. Sun. "Reinforcement Learning: Theory and Algorithms". Available here.
  • F. Bach. "Learning Theory from First Principles". MIT Press, 2024.*
  • P. Petersen, J. Zech. "Mathematical Theory of Deep Learning". Available here.*
  • M. Hutter, E. Catt, D. Quarel. "An Introduction to Universal Artificial Intelligence". Chapman & Hall, 2024.*
• Lean (skills):
  • J. Avigad, L. de Moura, S. Kong, S. Ullrich, et al. "Theorem Proving in Lean 4". Available here.
  • J. Avigad, P. Massot. "Mathematics in Lean". Available here. [Note: Tactics summary.]
  • D.T. Christiansen. "Functional Programming in Lean". Available here. [Note: Chapters 1-4.]

* I am not familiar with most of the content of the three starred references above. However, they are included here because they are excellent candidates for formalization.