Deep Learning, Goodfellow, Bengio, Courville, MIT Press, 2016
Deep Learning: An Introduction for Applied Mathematicians, Higham, Higham, SIAM Review, 2019
Deep Learning, LeCun, Bengio, Hinton, Nature, 2015
Neural Networks and Deep Learning, Michael Nielsen, 2015
DOE reports on scientific machine learning
Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence, Baker et al., 2019
Machine Learning and Understanding for Intelligent Extreme Scale Scientific Computing and Discovery, 2015
On the foundations of computational mathematics and the potential limits of AI, Anders Hansen, University of Cambridge, One World Seminar Series on the Mathematics of Machine Learning, July 15, 2020
Trainability and accuracy of artificial neural networks, Eric Vanden-Eijnden, Courant Institute, NYU, One World Seminar Series on the Mathematics of Machine Learning, July 8, 2020
Towards a mathematical understanding of supervised learning, Weinan E, Princeton University, One World Seminar Series on the Mathematics of Machine Learning, July 1, 2020
On Langevin Dynamics in Machine Learning, Michael Jordan, UC Berkeley, Seminar on Theoretical Machine Learning, Institute for Advanced Study, June 11, 2020
The Decision-Making Side of Machine Learning - Computational, Inferential and Economic Perspectives, Michael Jordan, UC Berkeley, June 12, 2020
Scientific Machine Learning: Where Physics-based Modeling Meets Data-driven Learning, Professor Karen Willcox, University of Texas at Austin, Santa Fe Institute, May 2020
The Deep Learning - Applied Math Connection, Yann LeCun, NYU Courant Institute and Center for Data Science, SIAM Annual Meeting 2020
Deep Learning and Modeling: Taking the Best out of Both Worlds, Gitta Kutyniok, Technische Universität Berlin, IPAM - Deep Learning and Medical Applications, April 2, 2020
Understanding Deep Neural Networks: From Generalization to Interpretability, Gitta Kutyniok, Technische Universität Berlin, March 5, 2020
Hidden Physics Models: Machine Learning of Non-Linear Partial Differential Equations, Maziar Raissi, Brown University - IPAM Machine Learning for Physics and the Physics of Learning 2019 Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature
Sparse Modelling of Data and its Relation to Deep Learning, Michael Elad, Technion, November, 2019
Dynamics and Generalization in Deep Neural Networks, Tomaso Poggio, MIT, November, 2019
Multiscale Models for Image Classification and Physics with Deep Networks, Stéphane Mallat, École Normale Supérieure, November 28, 2019
Machine learning based multi-scale modeling, Weinan E, Princeton University, IPAM - Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences, October 15, 2019
An Information Theoretic Approach to Validate Deep Learning-Based Algorithms, Gitta Kutyniok, Technische Universität Berlin, IPAM Machine Learning for Physics and the Physics of Learning 2019 Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature October 30, 2019
Mathematical and Computational Aspects of Machine Learning, October 9, 2019
Deep Neural Networks Motivated By Differential Equations part 1, (part 2), Lars Ruthotto, Emory University, October 8, 2019
Theory of Deep Learning, Gitta Kutyniok, Technische Universität Berlin, May 15, 2019
Learning Physics with Deep Neural Networks, Stéphane Mallat, École Normale Supérieure, October 17, 2018
Expressivity of Deep Neural Networks, Gitta Kutyniok, Technische Universität Berlin, April 18, 2018
The mathematics of machine learning and deep learning, Sanjeev Arora, Princeton University, ICM2018
Optimal Approximation with Sparsely Connected DNNs, Gitta Kutyniok, Technische Universität Berlin, August 3, 2017
High-Dimensional Learning and Deep Neural Networks, Stéphane Mallat, École Normale Supérieure, March 31, 2016
SIAM Conference on Mathematics of Data Science (MDS20) Talks Playlist
SIAM CSE 2019 Talks and Slides on Scientific Machine Learning
Solving Inverse Problems with Deep Learning, Lexing Ying, Stanford University, May 29, 2020
Learning to Solve Inverse Problems in Imaging, Rebecca Willett, University of Chicago, May 29, 2020
Data-Driven Methods for Inverse Problems, Ozan Öktem, KTH - Royal Institute of Technology, May 29, 2020
A Mathematical Perspective of Machine Learning, Weinan E, Princeton University, May 22, 2020
Deep Neural Networks for High-Dimensional Parabolic PDEs, Christoph Reisinger, University of Oxford, May 22, 2020
Scientific AI: Domain Models with Integrated Machine Learning, Chris Rackauckas, JuliaCon 2019, July 2019
Universal Differential Equations for Scientific Machine Learning, Florida State University (FSU) Scientific Computing Colloquium on February 19, 2020
MSML2020 - Mathematical and Scientific Machine Learning Conference, July 20-24, 2020
Molecular Simulation with Machine Learning, July 13-14, 2020
SIAM Conference on Mathematics of Data Science (MDS20), May 4 - June 30, 2020
International Workshop on Scientific Machine Learning, January 8-10, 2020
Workshop on Mathematical Machine Learning and Applications, TBD, 2020
DeepMath Conference, October 31 - November 1, 2019
PIMS CRG Summer School: Deep Learning for Computational Mathematics, July 22-25, 2019
Scientific computation using machine-learning algorithms, April 25-26, 2019
Scientific Machine Learning, January 28-30, 2019
Machine Learning for Science Workshop (ML4Sci), September 4-6, 2018 (Conference site)
The Center for Scientific Machine Learning, The Oden Institute, University of Texas at Austin
https://www.oden.utexas.edu/research/centers-groups/scientific-machine-learning/
FG Applied Functional Analysis, TU Berlin
https://www.math.tu-berlin.de/fachgebiete_ag_modnumdiff/angewandtefunktionalanalysis/v_menue/afg/
Argonne National Laboratory Machine Learning Group