Miscellaneous

Good Books

• Dayan, Peter, and Abbott, L. F.. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. United Kingdom, MIT Press, 2001.

  • • This seminal book provides a comprehensive tour of the field.

• Hyvärinen, Aapo, et al. Natural Image Statistics: A Probabilistic Approach to Early Computational Vision. Germany, Springer London, 2009.

  • • A useful resource for studying visual processing from a statistical and probabilistic perspective.

    • Book website: https://www.cs.helsinki.fi/u/ahyvarin/natimgsx/ 

• Haykin, Simon. Adaptive Filter Theory: International Edition. United Kingdom, Pearson Education, 2014.

  • • Topics in signal processing and statistics, relevant to the learning process in neural networks, are covered in an accessible fashion.

• Sayama, Hiroki. Introduction to the Modeling and Analysis of Complex Systems. United States, Open Suny Textbooks, 2015.

  • • A great introduction to graph theory and complex systems theory.

    • Book website: https://open.umn.edu/opentextbooks/textbooks/233 

• Delft, Jan von, and Altland, Alexander. Mathematics for Physicists: Introductory Concepts and Methods. United Kingdom, Cambridge University Press, 2019.

  • • Helpful for sharpening the mathematical lexicon of those with backgrounds in applied disciplines such as physics (such as myself).