# Machine Learning

## minimc

This is a test library to provide reference implementations of MCMC algorithms and ideas. The basis and reference for much of this library is from Michael Betancourt’s wonderful A Conceptual Introduction to Hamiltonian Monte Carlo. The highlight of the library right now is the ~15 line Hamiltonian Monte Carlo implementation (which relies on an 8 line integrator). This is commented and documented, with an aim to be instructive to read.

## imcmc

imcmc (im-sea-em-sea) is a small library for turning 2d images into probability distributions and then sampling from them to create images and gifs.

## PyMC3

I am a contributor to PyMC3, a “Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms.”

## Compare Cross Country Courses

An offshoot of another project, this allows you to compare times between most collegiate cross country courses.

## Minimal Machine Learning Visualization Example II

A demonstration of using Flask, React, and d3js to visualize machine learning models. This is a port of a previous project from Angular to React.

## Cross Country Predictions

Using hundreds of thousands of historical cross country running results to make predictions about future meets. The page is updated more than weekly during the season.

## Predicting March Madness

For the second year in a row, I took part in Kaggle’s contest to predict March Madness winners. The code for the actual model is not very expository (get in touch if you are interested), but I also built a friendlier page to query predictions interactively at the link.

## Bayesian Updating

An interactive page for demonstrating and experimenting with the beta distribution. Built with AngularJS and d3js.

## A Bayesian Approach to L1 and L2 Regularization

An essay on building linear regression models. It is converted from notes for a talk I gave at Rice University in September 2014. Contains lots of pictures, lots of interactivity, and a modest amount of math. As a bonus, everything is typeset with KaTeX.

## Linear Regression Demo

A demonstration of linear regression, overfitting, normalization, and regularization. Allows you to choose data from a distribution, and interactively fit polynomials to the data using least squares, ridge, or Lasso regression.