Basics of Machine Learning¶
The following elementary concepts of machine learning are introduced:
- supervised training with data
- structure of training data
- (linear) regression
- classification (logistic regression)
- maximum likelihood principle
- gradient descent
- cost function and loss
- squared error loss
- cross entropy loss
- linear separability, non-linear transformations with basis functions
- overfitting, underfitting, bias-variance tradeoff
- learning curves
- regularization
- validation