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Machine Learning in R
March 19 @ 2:00 pm - 4:00 pm
Modern Languages Building (MLB), Room 2001A
In this workshop, we’ll first discuss core machine learning concepts such as: choosing loss functions and evaluation metrics; splitting the data into training, validation, and testing sets; and cross-validation patterns for tuning hyper-parameters. Next, we’ll apply these concepts to train models for identifying isolated letters from speech (https://archive.ics.uci.edu/
Specifically, we’ll apply the elastic net (a generalization of ridge and lasso regression), random forests, and gradient boosting to this task. We’ll briefly discuss each model/method but our primary focus will be on understanding the core functionality of the related R packages (glmnet, randomForests, xgboost) and tuning associated hyper-parameters.