dan-veltri/ace-intro-to-ml-algorithms

Intro to ML Algorithms Course Materials for ACE Uganda # Class Update (Feb 25) Fixed typo in PowerPoint slides and added OpenOffice/LibreOffice formatted slides with fewer animations that should work better with the classroom PCs. Fixed the example SVM R script. # Class Update (Feb 24) Updated slides, plus R code for RandomForest and PCA. The "swiss-roll" data set has also been added to the data folder. # Class Update (Feb. 11) * Here's a nice article on selecting an appropriate *k* for k-means clustering using R: r-bloggers.com. I've also updated the R-code example in the repo with a new section showing one way to select *k* using the "elbow method." * Another nice article detailing how to run and interpret the output of linear regression in R (note many of the output statistics discussed are also provided by Weka): r-statistics.co # Into to Machine Learning Algorithms Part 1-3 Course Materials are now online. Today we will be running examples using Weka and RStudio (feel free to use one or both of these) If you are using a classroom computer, please make sure you can open the programs. If you are using your own laptop, please install one if you do not have it already: * Weka (requires Java): cs.waikato.ac.nz + Launch from terminal where Weka is located with: `java -jar weka.jar` * R (programming language): cran.r-project.org * RStudio Desktop: rstudio.com NOTE: `.Rmd` files are RMarkdown files and should be opened in RStudio rather than regular R. These are interactive notebook files similar to Jupyter notebooks for Python. If you're interested in learning more about RMarkdown I have slides from a previous class I've taught on it available here.