Resources

A collection of textbooks and resources that have been recommended to me or that I’ve found useful. I clearly have not gone through every resource listed here.

I don’t maintain this page actively. Please contact me at schitrak@ualberta.ca if there’s a link issue or a resource you’d like added!

One day I will write a road-map on how I would read through this list if I wanted to learn a certain topic.

Linear Algebra

Mathematical Analysis

Note: Unless you specifically need measure theory, and you’re only curious about probability theory, get a measure-theoretic probability book instead (e.g., Williams, Billingsley, Capiński) as pure measure theory can be dry.

Note: For topology read up on separability, metrization, Urysohn metrization theorem, Tychonoff theorem, and especially Baire category theorem. For the most part, topology chapters in analysis textbooks is more than enough!

Topology chapters in analysis books:

  • Real Analysis and Probability by Dudley — Chapters 1-2, Appendix A/E
  • Real Analysis by Folland — Chapter 4
  • Principles of Mathematical Analysis by Rudin — Chapter 2
  • Real Analysis by Royden — Has good chapters
  • Measure Theory by Bogachev — Advanced and topologically flavoured

Note: This is for curiosity.

Probability Theory

Note: Imperative to understand low-dimensional statistics and matrix inequalities first. This is a heavy topic.

Statistics

Numerical Analysis:

Statistical Computing & Monte Carlo:

Optimization:

Learning

Note: I don’t have any posted resources on Large Language Models (LLM) since the field is changing very fast.

Practical Deep Learning:

Courses and Videos:

Discrete Mathematics