Frank Noé: "Fundamentals of Artificial Intelligence and Machine Learning" (Part 2/2)
Watch part 1/2 here: https://youtu.be/5f-u0hgiLXw
Mathematical Challenges and Opportunities for Autonomous Vehicles Tutorials 2020
"Fundamentals of Artificial Intelligence and Machine Learning" (Part 2/2)
Frank Noé - Freie Universität Berlin
Institute for Pure and Applied Mathematics, UCLA
September 17, 2020
For more information: https://www.ipam.ucla.edu/avtut
Видео Frank Noé: "Fundamentals of Artificial Intelligence and Machine Learning" (Part 2/2) канала Institute for Pure & Applied Mathematics (IPAM)
Mathematical Challenges and Opportunities for Autonomous Vehicles Tutorials 2020
"Fundamentals of Artificial Intelligence and Machine Learning" (Part 2/2)
Frank Noé - Freie Universität Berlin
Institute for Pure and Applied Mathematics, UCLA
September 17, 2020
For more information: https://www.ipam.ucla.edu/avtut
Видео Frank Noé: "Fundamentals of Artificial Intelligence and Machine Learning" (Part 2/2) канала Institute for Pure & Applied Mathematics (IPAM)
Показать
Комментарии отсутствуют
Информация о видео
30 сентября 2020 г. 0:11:13
01:05:22
Другие видео канала
Jaafar El-Awady - dislocation in high thermomechanical condition in Additive Manufacturing of Alloys
Vikram Gavini - Fast, Accurate and Large-scale Ab-initio Calculations for Materials Modeling
Bistra Dilkina - Machine Learning for MIP Solving - IPAM at UCLA
Amit Acharya - Slow time-scale behavior of fast microscopic dynamics - IPAM at UCLA
Eran Rabani - Stochastic Density Functional Theory - IPAM at UCLA
Deanna Needell - Using Algebraic Factorizations for Interpretable Learning - IPAM at UCLA
Xavier Bresson - Learning to Untangle Genome Assembly Graphs - IPAM at UCLA
Jack Gilbert: "Microbiome of the Built Environment"
John Harrison - Formalization and Automated Reasoning: A Personal and Historical Perspective
Raymond Clay - Machine Learning in Equation of State and Transport Modeling at Extreme Conditions
Rose Yu - Incorporating Symmetry for Learning Spatiotemporal Dynamics - IPAM at UCLA
Yongsoo Yang - Neural network-assisted atomic electron tomography - IPAM at UCLA
Margaret Murnane - Attosecond Quantum Technologies for Advanced Materials Metrologies - IPAM at UCLA
Albert Fannjiang - From Tomographic Phase Retrieval to Projection Tomography - IPAM at UCLA
Thomas Swinburne - Learning uncertainty-aware models of defect kinetics at scale - IPAM at UCLA
Kevin Kelly - Machine Learning Enhanced Compressive Hyperspectral Imaging - IPAM at UCLA
Demetri Psaltis - Machine Learning for 3D Optical Imaging - IPAM at UCLA
Paola Gori-Giorgi - Large-coupling strength expansion in DFT and Hartree-Fock adiabatic connections
Bohua Zhan - Verifying symbolic computation in the HolPy theorem prover - IPAM at UCLA
Xiantao Li - A stochastic algorithm for self-consistent calculations in DFT - IPAM at UCLA
Pascal Van Hentenryck - Fusing Machine Learning and Optimization - IPAM at UCLA