Intro to R: Machine Learning & Web Apps | College of Arts

Intro to R: Machine Learning & Web Apps

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Instructors:     

Dr. Rachel Starry University of Buffalo (SUNY)

Paul Barrett     University of Guelph

Nathan Taback University of Toronto

 

Description:

This course begins with an introduction to the R programming language and then moves into basic operations in machine learning, statistical methods, and web applications. The course is designed for digital humanities practitioners of all backgrounds and assumes no prior experience with R. We begin with a gentle introduction to R in the RStudio environment, including common syntax and data structures, with an emphasis on Tidyverse packages for importing, exploring, and visualizing data. Following that, we will introduce concepts in data science, statistics, and machine learning as they are relevant to DH. Students will follow along, and modify, sample scrips that allow them to experiment with data mining, sentiment analysis, and topic modeling. Students will also learn the basics of creating interactive Shiny web apps. The course format emphasizes active learning through hands-on practice and collaborative project development. Students will also be asked to critically reflect upon and discuss how statistical analysis affects traditional humanities insight as how machine learning reinforces forms of cultural bias.

 

Intended Audience:

A wide variety of participants would benefit from this course, from undergraduate and graduate students in humanities disciplines interested in learning new skills, to researchers and instructors seeking to enhance their research workflows or gain exposure to R as a tool for teaching DH methods, to librarians and technologists who would like to become more familiar with R in order to better support their user communities. In order to benefit a diverse audience, this course tries to find a balance between offering a high-level overview of the possibilities for using R in digital humanities projects, and giving participants significant time to practice with a few specific packages and workflows, emphasizing text wrangling and analysis and the creation of interactive maps and web interfaces.