Basic Information
The Students Will Learn Basic Tools in Algebra and How to Use Them In Order to Design and Analyze Neural Networks That Can Be Applied To Complex Objects Such As Sets, Graphs, Point Clouds and More. The Course Will Begin With an Introduction to Groups, The Main Algebraic Structure Used in The Field. in The Second Part of The Course, We Will Use The Knowledge We Have Gained to Review Central Methods For Designing Architectures and to Analyze a Variety of Architectures That Have Been Presented in Recent Years. List of Topics# Groups - Definitions and Basic Results. Group Actions. Basic Concepts in The Representations of Finite Groups and The Theory Of Characters. Basic Concepts in The Invariant Theory. Deep Learning On Complex Objects# Canonization, Symmetrization And Intrinsic Methods, Characterization of Linear Layers, Convolution On Groups. Examples# Images, Groups, Graphs and Three-dimensional Objects. Expressive Power and Universality. Learning Outcomes# At The End of The Course, Students Will Be Able To#_ 1. Explain and Use Basic Concepts in Group Theory._ 2. Explain and Use Basic Concepts in The Field of Representations Of Finite Groups. 3. Design, Analyze and Implement Deep Architectures For Strucured Data._
Faculty: Electrical and Computer Engineering
|Undergraduate Studies
|Graduate Studies
Pre-required courses
46195 - Machine Learning or 236756 - Introduction to Machine Learning