I wrote curriculum and code for the Applied and Computational Mathematics program at BYU. The writing I did is for the Python based labs associated with this program. My areas of work within this project were data science tools, machine learning tools, ODE and PDE solvers, and calculus of variations and optimal control.
I wrote a lab that describes the mathematics behind Naïve Bayes classifiers and instructs students on how to create an NB classifier from scratch. This includes using maximum likelihood estimation (MLE) and Bayesian inference to predict parameters in feature distributions. This lab also included instructions on using the naïve Bayes methods in scikit-learn. I also wrote a lab that describes the mathematics behind optimal control and instructs students on how to code solutions to optimal control problems, such as obstacle avoidance.
I managed others that were also writing curriculum, including advising on pedagogical methods and reviewing instructional material.