Description

This lecture decomposes machine learning (ML) into three main components :
data, model and loss. Every ML method uses specific design choices for data
(representation), model and loss function. A main design principle for ML
methods is empirical risk minimization (ERM). ERM learns optimal model
parameters by minimizing the average loss on a training set which consists of
carefully chosen data points.

|Lecture could not be delivered in person due to a sick leave.]
Period15 Oct 2024
Event titleUnite! Research School
Event typeWorkshop
LocationGrenoble, FranceShow on map