Teaching
As a member of the INFORMS student chapter at OU, I've had the priveledge of hosting multiple seminars and tutorials on machine learning and tools to be used for research. Here, you'll find links to Google Colab notebooks that I've used for some of my seminars and lectures given to some of my fellow students, which showcase practical examples and hands-on learning experiences. Each notebook includes not just code and examples, but also explanations and links so that students can continue learning on their own.
Supervised Machine Learning Tutorial
This notebook goes over the basics of machine learning, with a special focus on supervised machine learning. I demonstrate how to use python load and manipulate data, graph the results, and perform basic regression and classification. Also included are a variety of links to other websites, books, and blogs that students can use to continue learning.
You can access the notebook here.
Unsupervised Machine Learning Tutorial
Another branch of machine learning is unsupervised machine learning, in which the models learn about the data without any labels on the data. This can consist of finding unknown patterns in the data, outlier analysis, and dimensionality reduction.
You can access the notebook here.
Interpretability Tutorial
In this notebook, I cover the fundamental concepts of interpretability and explore various techniques including local, global, model-intrinsic, and model-agnostic methods. The tutorial dives into practical tools like Permutation Importance, LIME, SHAP, and Partial Dependence Plots, offering insights into how to interpret machine learning models and make them more transparent..
You can access the notebook here.