Mentoring

Directed Reading Program

I served as a graduate student mentor for the Directed Reading Program in 2022 Spring. Topic of my session is Probabilistic perspectives in machine learning. Following are some of the handouts I wrote:

• Supervised and unsupervised learning, Basic concepts in ML, Fundamentals in probability [pdf]

• Entropy, KL divergence, Maximum Likelihood Estimation [pdf]

• MLE for Linear Regression, MLE for Logistic Regression [pdf]

• Mixture Normal, EM algorithm, EM for Mixture Normal [pdf]

The contents are mostly from the book Machine Learning: A probabilistic Prospective by Kevin Patrick Murphy, and from the lecture notes of the course Selections of Frontiers in Statistics lectured by Zhou Yu, ECNU.

Madison Experimental Mathematics Lab

I served as a graduate student instructor for the Experimental Mathematics Lab in 2022 Spring. The project that I supervised is Vectors with smallest slopes. Here is the poster. Brief intro can be found here.