Algebraic Geometry and Machine Learning
Tsinghua Sanya International Mathematics Forum
January 26th-29th, 2021
Topics (not limited to):
Applications of Algebraic Geometry* in Machine Learning**
Applications of Machine Learning in Algebraic Geometry
*Algebraic Geometry is broadly defined: pure, applied and computational (algorithmic: symbolic and numerical), inspired by or applied in scientific areas, etc.
**Machine Learning is also broadly defined: supervised, unsupervised, semi-supervised, self-supervised, reinforcement learning, etc.
The basic premise of this workshop is to provide a platform to the research that aims to combine machine learning and algebraic geometry.
We thank Tsinghua Sanya International Mathematics Forum for hosting the workshop and providing us administrative support.
Jonathan Hauenstein (University of Notre Dame)
Yang-Hui He (University of Oxford and City, University of London)
Alexander Kasprzyk (University of Nottingham)
Dhagash Mehta (The Vanguard Group)
Shing-Tung Yau (Harvard University, Tsinghua University and BIMSA)