A CRASH COURSE ON USING MACHINE LEARNING METHODS EFFECTIVELY IN PRACTICE

• Event organized jointly by SSA and MAPS https://www.statsoc.org.au/event-5028135
• Workshop based on Chapt 2 and 3 of The Mathematical Engineering of Deep Learning

Material

• The easy way to get the material is to download the zip file of this archive

• Note book used during the workshop

  • Part 1: supervised learning
  • Part 2: unsupervised learning

R user

Please make sure that you have recent R and RStudio installed.

• R : https://cran.r-project.org/
• RStudio : https://www.rstudio.com/products/rstudio/download/preview/

R Packages

• R packages to be installed before workshop

install.packages("dplyr")
install.packages("ROCR")
install.packages("caret")
install.packages("ROCit")
install.packages("MASS")
install.packages("glmnet")
install.packages("tidyverse")
install.packages("ggplot2")
install.packages("ggpubr")
install.packages("animation")
install.packages("mvtnorm")
install.packages("jpeg")
install.packages("ruta")
install.packages("rARPACK")
install.packages("FactoMineR")
install.packages("keras")
install.packages("IMIFA")
install.packages("nnet")

Data file to download from DATA folder

• versatile-boundaries.RData
• Breast_cancer.RData
• train-images.idx3-ubyt
• train-labels.idx1-ubyte
• t10k-images.idx3-ubyte
• t10k-labels.idx1-ubyte
• data-poly.Rdata

Python user

we use google collab to share code:

• Part 1: supervised learning code
  • Lasso model code
• Part 2: unsupervised learning code