Programming Languages

Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data. During this two part workshop, we will discuss basic features of supervised machine learning algorithms including k-nearest neighbor, linear regression, decision tree, random forest, boosting, and ensembling using the tidymodels framework. To social scientists, such methods might be critical for investigating evolutionary relationships, global health patterns, voter turnout in local elections, or individual psychological diagnoses.

Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided.

The goal of this workshop is to build intuition for deep learning by building, training, and testing models in Python. Rather than a theory-centered approach, we will evaluate deep learning models through empirical results.