Basic Information
General Introduction to Deep Learning and Convolutional Networks. Neural Collapse. Fourier Signal Processing, Wavelet Analysis, Semi-discrete Wavelet Transforms. Wavelet Scattering Transforms# Translation Invariance, Contraction, Energy Conservation, Deformation Stability, and The Relation to General Convolutional Networks. Wavelet Scattering Transforms of Stochastic Processes. Graph Convolutional Networks# Graph Fourier Transform, Spectral Graph Convolutional Networks, Message Passing Networks, and Analysis Of Graph Neural Networks. Learning Outcomes# at The End of The Course The Student Will Be Able To# 1. Aanalyze Convolutional Networks From The Point of View of Signal Processing. 2. Generalize Convolutional Networks to Graphs. 3. Read Research Papers On Advanced Topics of Mathematical Theory Of Convolutional Networks._
Faculty: Mathematics
|Undergraduate Studies
|Graduate Studies
Pre-required courses
(94411 - Probability (ie) and 104022 - Differential and Integral Calculus 2m) or (94412 - Probability (advanced) and 104032 - Calculus 2m) or (104013 - Differential and Integral Calculus 2t and 104034 - Introduction to Probability H) or (104222 - Probability Theory and 104295 - Infinitesimal Calculus 3)