MATH-CS COMPASS Bridging Pure & Applied Mathematics

Hello, I'm Yusuke Yokota. Welcome to MATH-CS COMPASS, where pure & applied mathematics meet computer science.

I created this site after noticing that while many computer science people rely heavily on mathematical concepts, they often lack a clear overview of these ideas, especially in today's AI-driven era. On the other hand, those deeply involved in pure mathematics sometimes overlook the practical applications of their work.

While pure mathematicians often prefer formal, rigorous explanations, and computer scientists tend to focus more on experimental results, I believe that understanding how mathematics translates into real-world applications can enrich both theoretical work for mathematicians and innovation for computer scientists.

This platform is designed to bridge that gap: it offers an accessible look at the math world, highlighting connections that are especially relevant in the current landscape of AI and technology. It's not purely theoretical math, nor is it overly casual—it's a resource intended to inspire both math enthusiasts and computer scientists alike.

Exploring these intersections, you'll gain valuable insights that support your academic journey and professional endeavors. If you have any suggestions, requests, or inquiries, please feel free to use the contact form below. Your input is invaluable in helping improve this site for everyone.

I would also like to express my deep appreciation to the Western Washington University Mathematics and Computer Science Departments' students and faculty members. Their passion and inspiration have been a tremendous source of motivation in creating this platform after I graduated.

Thank you for visiting, and enjoy your exploration!

(*Please note that the content on this site is still a work in progress. More resources are on the way.)

Update Log

Explore Topics

I - Linear Algebra

Explore foundations of modern mathematics & computer science.

II - Calculus to Optimization & Analysis

Explore optimization techniques and mathematical analysis.

III - Probability & Statistics

Explore probability theory and statistical methods.

IV - Discrete Mathematics & Algorithms

Explore graph theory, combinatorics, the theory of computation, and algorithms.

V - Machine Learning

Temporarily closed. Most of the mathematical topics for ML will be covered by Section I - IV.

References

Books

  • David C. Lay. Linear Algebra and Its Applications. 4th ed., Pearson Education, Inc., 2012.
  • Diestel, Reinhard. Graph Theory. 5th ed., Springer, 2017.
  • Merris, Russell. Combinatorics. 2nd ed., Wiley, 2003.
  • Murphy, Kevin P. Probabilistic Machine Learning: An Introduction. The MIT Press, 2022.
  • Murphy, Kevin P. Probabilistic Machine Learning: Advanced Topics. The MIT Press, 2023.
  • O'Searcoid, Mícheál. Metric Spaces. Springer Undergraduate Mathematics Series, Springer, 2006.
  • Sipser, Michael. Introduction to the Theory of Computation. 3rd ed., Cengage Learning, 2013.

Online Courses