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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/ml/datasets/isolet).

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.

Details

Date:
March 19
Time:
2:00 pm - 4:00 pm
Event Category:

Organizer

CSCAR
Phone:
734-764-7828
Website:
https://cscar.research.umich.edu/

Other

James Henderson