Statistics and Machine Learning

Introduction

Refer to Artificial Intelligence in Healthcare / Machine Learning Resources for general information about machine learning. This section provides instructions for getting started with some of the common statistics and machine learning Julia packages.

Getting Started

• Start Julia REPL (Read/Evaluate/Print/Loop) by typing the following in Terminal or PowerShell:

$ julia

• Go into REPL mode for Pkg, Julia’s built in package manager, by pressing ‘]’:

julia> ]
(@v1.4) pkg>

• Update package repository in Pkg REPL

(@v1.4) pkg> update)

• Add packages in Pkg REPL

(@v1.4) pkg> add CSV
(@v1.4) pkg> add DataFrames
(@v1.4) pkg> add Distributions
(@v1.4) pkg> add HypothesisTests

• Get back to the Julia REPL and exit by pressing backspace or ^C

(@v1.4) pkg>
julia> using CSV
julia> using DataFrames
julia> using Distributions
julia> using HypothesisTests
julia> exit()

Machine Learning Resources

Resource	Relevant Pages	Time	Access Requirements	Notes
MLJ Home	About	N/A	None	Language: Julia
Data Science Tutorials	Getting Started with MLJ	N/A	None, Jupyter notebook for tutorials	Language: Julia 10 steps/pages Walk-through of process (choosing model; fit, predict, transform; tune; ensemble, etc.)
Introduction to Statistical Learning with MLJ	N/A	None, Jupyter notebook for tutorials	Language: Julia 8 labs e.g. Lab 3 Linear Regression
End to end examples with MLJ	N/A	None, Jupyter notebook for tutorials	Language: Julia 11 total, 9 simple e.g. Breast Cancer Wisconsin
JuliaCon 2020 (YouTube video)	Follow-along Tutorials	~ 3.5 hrs	None	Language: Julia

Resources

• Artificial Intelligence in Healthcare / Machine Learning Resources

• Julia Cheat Sheet

• Julia Stats

• Packages

  • CSV.jl

  • DataFrames.jl

  • Distributions.jl

  • HypothesisTests.jl

• Statistics Module

• Random Number Generator