StatsCrashCourse

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StatsCrashCourse

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Tutorials for a crash course in statistics and machine learning for biologists

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# StatsCrashCourse

Tutorials for a crash course in applied statistics and machine learning in R for biologists. Inspired by chapters from the book *An Introduction to Statistical Learning* by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibsharini.

Tutorials are written in R Markdown with R Studio.

# Add Chapters with R Studio

1. Create a Github account if you don't already have one
2. Set up Git and GitHub with R Studio, as outlined in [this link](https://support.rstudio.com/hc/en-us/articles/200532077-Version-Control-with-Git-and-SVN)
3. Make a new project (File-> New Project) and select version control with Git
4. Set a local directory and link to the GitHub site: https://github.com/ColauttiLab/StatsCrashCourse
5. Email robert.colautti -->at<-- Queensu.ca with your github user name for writing privileges.

# Lessons:

## Schedule:

|Week | Date | Topic | Presenter 
|-----|------|-------|----------
|1 | Jan 24 | Linear Models | Rob
|2 | Jan 31 | Classificaion | Ryan
|3 | Feb 7 | Permutation | Rob
||Feb 14 | **N/A** |
||Feb 21 | **Reading Break** |
|4 | Feb 28 | Model Selection | Sam
|5 | Mar 7 | Nonlinear Models | Regan
||Mar 14 | **N/A** |
|6 | Mar 21 | Classification Trees | Ryan
|7 | Mar 28 | Support Vector Machines | Rob
|8 | Apr 4 | Clustering Methods | Jaimie

## 1. Basic Linear Regression Models (Rob)

Introduction to linear models: [1_LinearModels.html](1_LinearModels.html)

## 2. Classification (Ryan)

Classification models: [2_Classification.html](2_Classification.html)

And a more detailed/comprehensive version: [2_Classification_Comprehensive.html](2_Classification_Comprehensive.html)

## 3. Resampling Methods (Rob)

Bootstrap and permutation models: [3_Permutations.html](3_Permutations.html)

## 4. Linear Model Selection (Sam)

Subset selection, shrinkage, dimension reduction, ridge regression and the lasso [4_ModelSelection.html](4_ModelSelection.html)

## 5. Nonlinear Regression Models (Regan)

Polynomial regression, splines, and generalized additive models [5_NonlinearModels.html](5_NonlinearModels.html)

## 6. Tree-based Classification (Ryan)

Decision Trees, Bagging, Random Forests, Boosting
[6_Classification_Trees.html](6_Classification_Trees.html)

## 7. Support Vector Machines (Rob)

Machine learning algorithms for classification from linear predictors
[7_Support_Vector_Machines.html](7_Support_Vector_Machines.html)

## 8. Clustering Methods (Jaimie)

Principal Components Analysis, K-means & hierarchichal clustering

[8_Tutorial_Unsupervised_Learning.html](8_Tutorial_Unsupervised_Learning.html)

StatsCrashCourse

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