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The Series Finale of Online Education: Coursera Acquires Udemy

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Alan Turing Institute
Statistical Physics and Learning - Florent Krzakala, Sorbonne Université

Explore the intersection of statistical physics and machine learning, examining algorithmic paradigms, high-dimensional models, and cross-fertilization between statistics and computer science in the era of Big Data.
- YouTube
- 41 minutes
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Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC)
Statistical Physics at 65

Explore advanced concepts in statistical physics, focusing on complex systems and interdisciplinary applications. Gain insights from leading experts in the field.
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- 3 hours 24 minutes
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Institut des Hautes Etudes Scientifiques (IHES)
A Statistical Physics Approach to the Sine Beta Process and Other Random Point Processes

Explore statistical physics approaches to Sine Beta processes and random point systems, focusing on universality properties and applications of DLR tools.
- YouTube
- 54 minutes
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Galileo Galilei Institute
Random Matrices and Statistical Physics - Lecture 2

Explore connections between random matrices and statistical physics, delving into advanced mathematical concepts and their applications in complex systems analysis.
- YouTube
- 1 hour 44 minutes
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Fields Institute
Probability, Conformal Invariance, and Critical Phenomena in Statistical Physics

Explore conformal invariance and critical phenomena in statistical physics, focusing on probabilistic approaches and their applications to complex systems.
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IPhT-TV
Statistical Physics of Optimal Transport

Explore statistical physics principles applied to optimal transport problems, unraveling complex systems through mathematical modeling and analysis.
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Statistical Physics of Stochastic Gradient Descent

Explore statistical physics principles applied to stochastic gradient descent, uncovering insights into machine learning optimization techniques.
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Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC)
Towards Understanding Social Hysteresis - Insights from Statistical Physics and Surprises

Explore social hysteresis through statistical physics, uncovering insights and surprises in complex societal dynamics.
- YouTube
- 53 minutes
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Hausdorff Center for Mathematics
Excursion Decompositions in Statistical Physics

Explore excursion decompositions in statistical physics models, including random walks, Ising models, Gaussian free fields, and XOR-Ising models, with representations as sums of measures with disjoint supports.
- YouTube
- 55 minutes
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Erwin Schrödinger International Institute for Mathematics and Physics (ESI)
Statistical Physics of Growing Systems

Explore statistical physics principles applied to growing biological systems, from cell colony growth and DNA replication coordination to epidemic spread and tissue development patterns.
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International Centre for Theoretical Sciences
Statistical Physics of Complex Ecosystems

Explore statistical physics principles applied to complex ecological systems and their emergent behaviors in biological contexts.
- YouTube
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International Centre for Theoretical Sciences
Statistical Physics of Growing Systems

Explore statistical physics principles applied to growing biological systems and their emergent behaviors in high-dimensional contexts.
- YouTube
- 56 minutes
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IAS | PCMI Park City Mathematics Institute
Statistical Physics Approach to Asymptotic Enumeration and Large Deviations in Random Graphs - Part 2

Explore statistical physics tools like Gibbs measures and partition functions to solve asymptotic enumeration problems and analyze large deviations in random graphs.
- YouTube
- 58 minutes
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IAS | PCMI Park City Mathematics Institute
Statistical Physics Approach to Asymptotic Enumeration and Large Deviations in Random Graphs - Part 1

Explore statistical physics tools like Gibbs measures and partition functions to solve asymptotic enumeration problems and analyze large deviations in random graphs.
- YouTube
- 58 minutes
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INI Seminar Room 2
Modeling the Genome: A View by a Physicist - A Statistical Physics Approach

Explore a physicist's perspective on genome modeling, examining chromatin folding mechanisms, 3D structural ensembles, and the role of neural networks in predicting chromosomal organization.
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