Deep Learning and Approximation Theory  196014





Lecture 
Exercise 
Laboratory 
Project or Seminar 
House Work 
Weekly Hours 
3 





Determination of the grade according to progress during the semester and the submission of the final thesis
Prerequisites:
 
(
  Probability Theory 
104222
 
  
and
 Infinitesimal Calculus 3 
104295
 ) 

or

(
  Probability (Advanced) 
094412
 
  
and
 Calculus 2M 
104032
 ) 

or

(
  Differential and Integral Calculus 2T 
104013
 
  
and
 Introduction to Probability H 
104034
 ) 

or

(
  Probability (Ie) 
094411
 
  
and
 Differential and Integral Calculus 2M 
104022
 ) 
The First Part of the Course Will Be a Short Introduction to Machine Part of the Course Will Focus Solely on the Approximation Aspect of Connected Networks, Universality of Convolutional Networks and Invariant Networks, Approximation Rates for Smooth Functions, Relationship Between Neural Networks and More Classical Function Bases (Finite Elements, Splines, Wavelets), and "Power of Depth" Results Which Point to Functions That Can Be Approximated More Efficiently by Deep Neural Nets Than by Other Models. Be Able to:
Theory. B. Be Skilled to Read a State of the Art Research on Approximation Properties of Deep Neural Networks and Will Be Able to Conduct Independent Research in This Field.
Times and places of examinations
02/2021
2021/2022 Spring Semester
examination time  day  date  Season 

 Monday  18.07.2022  à 
 Thursday  29.09.2022  á 
Timetable to semester 02/2021
2021/2022 Spring Semester
Room  Building  Hour  day  Lecturer  Exercise Lecture  no.  Registering Group 

  08:3011:30  Tuesday  Dr. Dym Nadav  Lecture  10  10 
TextbooksPublished  Publisher  Authors  Book 

2014  cambridge university press  shai shalevshwartz and shai bendavid  understanding machine learning: from theory to algorithem 
Created in 24/01/2022 Time 20:56:58