ScriptsToStudyByDay.org

Day 1: loading data, normalization, clustering

R	Python	Notes
LoadData.R	LoadData.py
NormalizeData.R	NormalizedData.py	RLE- and mean-center-normalization
Clustering.R	Clustering.py	k-means and hierarchical clustering

Day 2: pca, knn classification, overfitting, cross-validation, feature selection

R	Python	Notes
PCA_intro.R
PCA.R	PCA.py
KnnSim.R	KnnSim.py	compare resub vs. test performance on simulated data
KnnSimCV.R	KnnSimCV.py	show cross-validation (cv) removes resub bias
BadFeatSel.R	BadFeatSel.py	supervised feature selection must be done under cv
KnnGrid.R	KnnGrid.py	compare cv acc for varying k parameter on real data
KnnReal.R	KnnReal.py	t-test feature selection/extraction + knn on real data

Day 3: linear models, regularization, naive bayes

R	Python	Notes
TTesting.R	TTesting.py
PredictingGeneExpression.R	PredictionGeneExpression.py
WhyRegularize.R	WhyRegularize.py
LogisticReal.R	LogisticReal.py
LdaIsLikeLogistic.R

Day 4: svm, bootstrap, trees, random forests, boosting

R	Python	Notes
SvmReal.R	SvmReal.py
bootstrap_examples.R		mostly taken from package bootstrap examples
KnnSimBoot.R
RandomForestReal.R	RandomForestReal.py
AdaBoostReal.R	AdaBoostReal.py
CompareModelStrats.R	CompareModelStrats.py