Machine learning
(T)ask
Classification
Classification with missing inputs
Regression
Transcription
Machine translation
Structure output
Anomaly detection
Synthesis and sampling
Imputation of missing values
Denoising
Density estimation or PMF estimation
(P)erformance
Accuracy
Error rate
Cross validation
Overfitting - When the gap between the traning-error and test-error is too large
Underfitting - Not low enough traning-error
Regularization - any modifaction we make to a learning algortihem that is intended to reduce its generalization error but not its traning error
MSE with regularization
λ is a hyperparameter, λ forces the weights to become smaller
(MSE) Mean square error