Lectures:
Introduction, the perceptron algorithm
Logistic Regression, gradient descent, stochastic gradient descent
Confusion matrix, ROC curves, Support vector machines, Loss functions
Underfitting, overfitting, learning curves, cross validation
Variance-bias decomposition, Neural networks
Training Neural networks
Curse of dimensionality, dimensionality reduction, k-neighbours, principal component analysis
Using sklearn and pandas