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
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

2. Classification (Ryan)

Classification models: 2_Classification.html

And a more detailed/comprehensive version: 2_Classification_Comprehensive.html

3. Resampling Methods (Rob)

Bootstrap and permutation models: 3_Permutations.html

4. Linear Model Selection (Sam)

Subset selection, shrinkage, dimension reduction, ridge regression and the lasso 4_ModelSelection.html

5. Nonlinear Regression Models (Regan)

Polynomial regression, splines, and generalized additive models 5_NonlinearModels.html

6. Tree-based Classification (Ryan)

Decision Trees, Bagging, Random Forests, Boosting 6_Classification_Trees.html

7. Support Vector Machines (Rob)

Machine learning algorithms for classification from linear predictors 7_Support_Vector_Machines.html

8. Clustering Methods (Jaimie)

Principal Components Analysis, K-means & hierarchichal clustering

8_Tutorial_Unsupervised_Learning.html