Dear all,
From the 7th until the 11th of April, RoMaDS https://www.mat.uniroma2.it/~rds/events.php will host https://jschmidthieber.personalweb.utwente.nl/ https://jschmidthieber.personalweb.utwente.nl/Johannes Schmidt-Hieber https://jschmidthieber.personalweb.utwente.nl/ (University of Twente) with the mini-course
Statistical theory of deep learning
The schedule is as follows: Mon 14:00-16:30, Wed 14:00-16:30, Fri 14:00-16:30. All lectures will be held in Aula Dal Passo in the Math Department of university of Rome, Tor Vergata.
Here is the program for the three lectures:
Lecture 1. Intro and theory for shallow networks
Perceptron convergence theorem Universal approximation theorem Approximation rates for shallow neural networks Barron spaces Lecture 2. Theory for deep networks Advantages of additional hidden layers Deep ReLU networks Misclassification error for image deformation models Lecture 3. Theory of gradient descent in machine learning Optimization in machine learning Weight balancing phenomenon Analysis of dropout Benign overfitting Grokking
We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming:
https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwPU-iIPEmqet8NCg1%40thread.tacv2/1742807097614?context={%22Tid%22%3a%2224c5be2a-d764-40c5-9975-82d08ae47d0e%22%2c%22Oid%22%3a%22650fc4a8-4cec-4bd2-87bc-90d134074fe6%22}
The seminars are part of the Excellence Project MatMod@TOV.