Date/Time
Date(s) - 23/09/2024
3:30 pm - 4:30 pm

• Location: HH 302
• First Speaker Information
  • Speaker: Hannah Nardone
  • Title: Densities of Bounded Primes for Hypergeometric Series
  • Abstract: In this talk we will introduce hypergeometric series as solutions of a certain differential equation. These functions arise all throughout mathematics and physics.

We will discuss when the solutions of these equations can be reduced modulo primes, a question which has applications in number theory. In certain cases the answer to this questions has a precise formula, and this project focuses on finding an analogous formula in a related case.

• Second Speaker Information
  • Speaker: Kyle Sung
  • Title: Learning How Machines Learn
  • Abstract: Machine learning is changing the world, so let’s learn about its theory and applications. We briefly review linear regression and discuss fundamental questions motivating deep learning: what are the “best” models and how do we find them? Then, we’ll discover how combining linear regressions and nonlinear “activation” functions can form a neural network (NN): a type of function inspired by the makeup of our brains.

We conclude with some mathematical proofs and real-world results to learn more about NNs. This talk should be accessible to most second-year undergrads: first year multivariable differential calculus and linear algebra are handy prerequisites, but no knowledge of machine learning nor statistics is assumed. For those with more ML experience, some NN results may still be interesting.