Taught last in the spring of 2018, these are notes/assignments I created for a course on statistical-machine learning in R.
where am I?
If you need to stop by, feel free to! My desk location in Grissom is crudly mapped here. It's about 50/50 whether or not I'm actually at my desk (email first to guarentee a time) but I am there frequently.
The following notes are lab exercises which should go well with the EEE 595 curricula. They are a set of notes I have compiled which outline how to begin using R for data analysis, discussing some of the specifics of installing R, manipulating and loading basic data, and begining to use/evaluate models. The notes are are my own, so please contact prior to distribution. However a few of the questions are borrorwed in part from the ISLR book and should be attirubted appropriately.
A few of these lessons require specific data, however the general concepts are applicable to all types of data.
- Lab 0 notes: Introduction to using/installing R
- Lab 1 notes: Basic EDA in R
- Lab 2 notes: Plotting basic analyses
- Lab 3 notes: Basic modeling (lm, ridge, lasso)
- Lab 4 notes: Understanding public data, basic ggploting
- Lab 5 notes: Rmarkdown, literate programming, and LaTEX
- Lab 6 notes: GLM and backfitting
- Lab 7 notes: CART, Cross-Validation, RandomForest
- Lab 8 notes: MARS, EARTH, BART