Tutorials

These tutorials grew out of courses and bootcamps I have taught. They aim to be accessible — each one starts from scratch and builds intuition before formalism. All notebooks can be run in your browser via Google Colab with no local setup required.

Programming & Tools

Introduction to Python A Python primer for scientists with no prior programming experience. Covers the essentials — data types, control flow, functions — through short, self-contained examples designed to get you writing useful code quickly.   [GitHub]

Introduction to LaTeX A primer developed for students in Physics of Cognition who were new to LaTeX. Covers the essentials for scientific writing: document structure, math environments, figures, tables, and bibliography management with BibTeX.   [PDF]

Machine Learning & Statistics

Introduction to Machine Learning Created for the Emory IPoLS Scientific Programming Bootcamp and the Zewail City Winter School. Uses the MNIST dataset as a running example to build intuition for dimensionality reduction (PCA), classification (SVM), and generative models (autoencoders and variational autoencoders).   [GitHub]

Regression: From Linear Models to Reservoir Computing A lecture-based notebook that traces the regression story from ordinary least squares all the way to modern data-driven approaches: sparse identification of nonlinear dynamics (SINDy) and reservoir computing applied to chaotic systems.   [GitHub]

Mathematics

Mathematics Primers Developed for students in Physics of Cognition who needed a refresher on the mathematical tools used in the course. Covers linear algebra, probability and statistics, and an introduction to information theory — with an emphasis on building intuition over symbol-pushing.   [PDF]