Workshop – Machine Learning with R: A Hands-On Introduction
Wednesday, May 19 – Livestream
Full-day: 7:30am – 3:30pm PDT
People who want to use R to make predictions and discover valuable relationships in their data.
Knowledge Level: An introductory knowledge of R and machine learning is helpful, but not required.
R offers a wide variety of machine learning (ML) functions, each of which works in a slightly different way. This one-day, hands-on workshop starts with ML basics and takes you step-by-step through increasingly complex modeling styles. This workshop makes ML modeling easier through the use of packages that standardize the way the various functions work. When finished, you should be able to use R to apply the most popular and effective machine learning models to make predictions and assess the likely accuracy of those predictions.
The instructor will guide attendees on hands-on execution with R, covering:
- A brief introduction to R’s tidyverse functions, including a comparison of the caret and parsnip packages
- Pre-processing data
- Selecting variables
- Partitioning data for model development and validation
- Setting model training controls
- Developing predictive models using naïve Bayes, classification and regression trees, random forests, gradient boosting machines, and neural networks (more, if time permits)
- Evaluating model effectiveness using measures of accuracy and visualization
- Interpreting what “black-box” models are doing internally
Hardware: Bring Your Own Laptop
Each workshop participant is required to bring their laptop. Software installation instructions are available at http://r4stats.com/workshops/PAW2020. Attendees receive an electronic copy of the course materials and related R code after the workshop.
- Workshop starts at 7:30am PDT
- AM Break from 9:00 – 9:15am PDT
- Lunch Break from 11:00 – 11:45am PDT
- PM Break: 1:15 – 1:30pm PDT
- Workshops ends at 3:30pm PDT
Robert A. Muenchen, Manager of Research Computing Support, University of Tennessee
Robert A. Muenchen is the author of R for SAS and SPSS Users, and co-author of R for Stata Users and An Introduction to Biomedical Data Science. He is also the creator of r4stats.com, a popular web site devoted to analyzing trends in data science software, reviewing such software, and helping people learn the R language.
Bob is an ASA Accredited Professional Statistician™ who focuses on helping organizations migrate from SAS, SPSS, and Stata to the R Language. He has taught workshops on data science topics for more than 500 organizations and has presented workshops in partnership with the American Statistical Association, RStudio, DataCamp.com, and Revolution Analytics. Bob has written or co-authored over 70 articles published in scientific journals and conference proceedings and has provided guidance on more than 1,000 graduate theses and dissertations at the University of Tennessee.
Bob has served on the advisory boards of SAS Institute, SPSS Inc., BlueSky Statistics, and the Statistical Graphics Corporation. His contributions have been incorporated into SAS, SPSS, JMP, jamovi, BlueSky Statistics, STATGRAPHICS, and numerous R packages. His research interests include data science software, graphics and visualization, machine learning, and text analytics.