Videos

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Geometric and Machine Learning Methods for the Approximation of High-dimensional Dynamical Systems Thumbnail

Geometric and Machine Learning Methods for the Approximation of High-dimensional Dynamical Systems

Presenter
  • Mauro Maggioni
October 26, 2016
IPAM
Intelligent learning: similarity control and knowledge transfer Thumbnail

Intelligent learning: similarity control and knowledge transfer

Presenter
  • Vladimir Vapnik
March 30, 2015
IAS

Mathematics and Machine Learning in Cybersecurity

Presenter
  • Brian Witten
October 22, 2014
ICERM
Imaging and Machine Learning Thumbnail

Imaging and Machine Learning

Presenter
  • Eric Miller
September 24, 2013
IMA

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

Presenter
  • Lauren Sanders
March 24, 2024
ICERM
Decision-Aware Learning for Global Health Supply Chains Thumbnail

Decision-Aware Learning for Global Health Supply Chains

Presenter
  • Hamsa Bastani
April 3, 2023
IMSI
Regularity theory and uniform convergence in the large data limit of graph Laplacian eigenvectors on random data clouds Thumbnail

Regularity theory and uniform convergence in the large data limit of graph Laplacian eigenvectors on random data clouds

Presenter
  • Nicolas Garcia Trillos
May 7, 2020
SLMath
Understanding machine learning via exactly solvable statistical physics models Thumbnail

Understanding machine learning via exactly solvable statistical physics models

Presenter
  • Lenka Zdeborova
November 18, 2019
IPAM
Physics-Constrained Data-Driven Modeling : A Vision for Prediction, Design and Operation of Complex Systems Thumbnail

Physics-Constrained Data-Driven Modeling : A Vision for Prediction, Design and Operation of Complex Systems

Presenter
  • Karthik Duraisamy
March 8, 2018
IMA

Introduction to Topological Data Analysis and Machine Learning

Presenter
  • John Harer
October 23, 2014
ICERM
Jochen Garcke - Introduction to Machine Learning for Science and Engineering - IPAM at UCLA Thumbnail

Jochen Garcke - Introduction to Machine Learning for Science and Engineering - IPAM at UCLA

Presenter
  • Jochen Garcke
March 12, 2026
IPAM
A Neural Network Approach to Learning Steady States and Their Stability of Parametric Dynamical Systems Thumbnail

A Neural Network Approach to Learning Steady States and Their Stability of Parametric Dynamical Systems

Presenter
  • Xiaochuan Tian
May 15, 2024
IMSI