Summary and Schedule

This is a new lesson built with The Carpentries Workbench.

	Setup Instructions	Download files required for the lesson
Duration: 00h 00m	1. A Brief Introduction to Machine Learning	What is machine learning? What specific tools will this lesson cover?
Duration: 00h 20m	2. Linear and Logistic Regression	How can a model make predictions? How do we measure the performance of predictions?
Duration: 01h 10m	3. Decision Trees	What are decision trees? How can we use a decision tree model to make a prediction? What are some shortcomings of decision tree models?
Duration: 02h 05m	4. Random Forests	What are random forests? How do random forests improve decision tree models?
Duration: 03h 35m	5. Gradient Boosted Trees	What is gradient boosting? How can we train an XGBoost model? What is the learning rate?
Duration: 04h 45m	6. Cross Validation and Tuning	How can the fit of an XGBoost model be improved? What is cross validation? What are some guidelines for tuning parameters in a machine learning algorithm?
Duration: 06h 00m	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

Software Setup

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RStudio

Learners should have updated versions of R and RStudio installed. There are instructions for installing R, RStudio, and additional R packages in the R Ecology Lesson.

R Packages

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Please install the following R packages:

• tidyverse
• here
• rpart
• rpart.plot
• randomForest
• xgboost

Executing the following line of code in the R console will install all of these packages.

R

install.packages(c("tidyverse", "here", "rpart", "rpart.plot", "randomForest", "xgboost"))

You can view all of the packages installed on your system in the Packages tab in RStudio. Alternatively, you can print the names of any missing packages by executing the following command.

R

setdiff(c("tidyverse", "here", "rpart", "rpart.plot", "randomForest", "xgboost"),
        rownames(installed.packages()))

If the above command returns character(0), then you have successfully installed all of the packages that you need for this lesson.

Project and Data

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• Create a new RStudio project where you will keep all of the files for this workshop.
• In the project directory (where the .Rproj file is), create a subdirectory called data.
• You should then be able to download the data for the workshop using the following commands.

R

library(here)
download.file("https://www.openml.org/data/get_csv/49817/wine_quality.arff", 
               here("data", "wine.csv"))