Basic Information
This Course Will Introduce The Mathematical Foundations and Classical Learning Methods of High-dimensional Structures Such As Graphs, Sets, Grids, Meshes, Etc. The Course Will Cover Various Geometric Priors And Utilize Those to Learn Geometrical Structures, Analyzing And Improving The Expressiveness Towards Building Efficient Geometric Neural Networks. Learning Outcomes# at The End of The Course The Students Will# - Understand The Mathematical Foundations Beyond The Geometric Deep Geometric Learning. - Know The Current Methods and Deep Learning Architectures For High-dimensional Data. - Perform Small-scope Research Projects in The Field.
Faculty: Computer Science
|Undergraduate Studies
|Graduate Studies
Pre-required courses
46211 - Deep Learning or 97200 - Deep Learning or 236781 - Deep Learning On Computation
Course with no extra credit
97922 - Topics in Geometric Deep Learning
Semestrial Information
Weekly Hours
3 Academic Credit • 2 Lecture Hours • 1 Discussion Hours • 1 Project Hours
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Responsible(s)
Chaim Baskin
Registration Groups
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Weekly Hours
3 Academic Credit • 2 Lecture Hours • 1 Discussion Hours • 1 Project Hours
Go to Course Page
Responsible(s)
Alexander Bronstein
Notes
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במידה והקורס חסום, נא לפנות לד"ר חיים בסקין.
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