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Best Uni erlangen podcasts we could find (Updated October 2019)
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This lecture focuses on recent developments in image processing driven by medical applications. All algorithms are motivated by practical problems. The mathematical tools required to solve the considered image processing tasks will be introduced.
 
This lecture focuses on recent developments in image processing driven by medical applications. All algorithms are motivated by practical problems. The mathematical tools required to solve the considered image processing tasks will be introduced.
 
This lecture focuses on recent developments in image processing driven by medical applications. All algorithms are motivated by practical problems. The mathematical tools required to solve the considered image processing tasks will be introduced.
 
This lecture focuses on recent developments in image processing driven by medical applications. All algorithms are motivated by practical problems. The mathematical tools required to solve the considered image processing tasks will be introduced.
 
Towards New Understandings of Culture and Justice in the Context of Disaster Risk Reduction (DRR)
 
Towards New Understandings of Culture and Justice in the Context of Disaster Risk Reduction (DRR)
 
Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises: (multilayer) perceptron, backpropagation, fully connected neural networks loss functions and optimization strategies convolutional neural networks (CNNs) activation functions regularization strategies common practic ...
 
More information aboutnbsp;Machine Learning for Physicists: https://machine-learning-for-physicists.org
 
More information aboutnbsp;Machine Learning for Physicists: https://machine-learning-for-physicists.org
 
This lecture complements (and builds on top of) the lectures "Introduction to Pattern Recognition" and "Pattern Recognition". In this third edition, we focus on modeling of densities, and how to use these models for analyzing the data. Major topics of this lecture are regression, density estimation, manifold learning, hidden Markov models, conditional random fields, and random forests. The lecture is accompanied by exercises, where theoretical results are practically implemented and applied.
 
This lecture complements (and builds on top of) the lectures "Introduction to Pattern Recognition" and "Pattern Recognition". In this third edition, we focus on modeling of densities, and how to use these models for analyzing the data. Major topics of this lecture are regression, density estimation, manifold learning, hidden Markov models, conditional random fields, and random forests. The lecture is accompanied by exercises, where theoretical results are practically implemented and applied.
 
This lecture complements (and builds on top of) the lectures "Introduction to Pattern Recognition" and "Pattern Recognition". In this third edition, we focus on modeling of densities, and how to use these models for analyzing the data. Major topics of this lecture are regression, density estimation, manifold learning, hidden Markov models, conditional random fields, and random forests. The lecture is accompanied by exercises, where theoretical results are practically implemented and applied.
 
This lecture complements (and builds on top of) the lectures "Introduction to Pattern Recognition" and "Pattern Recognition". In this third edition, we focus on modeling of densities, and how to use these models for analyzing the data. Major topics of this lecture are regression, density estimation, manifold learning, hidden Markov models, conditional random fields, and random forests. The lecture is accompanied by exercises, where theoretical results are practically implemented and applied.
 
This lecture complements (and builds on top of) the lectures "Introduction to Pattern Recognition" and "Pattern Recognition". In this third edition, we focus on modeling of densities, and how to use these models for analyzing the data. Major topics of this lecture are regression, density estimation, manifold learning, hidden Markov models, conditional random fields, and random forests. The lecture is accompanied by exercises, where theoretical results are practically implemented and applied.
 
This lecture complements (and builds on top of) the lectures "Introduction to Pattern Recognition" and "Pattern Recognition". In this third edition, we focus on modeling of densities, and how to use these models for analyzing the data. Major topics of this lecture are regression, density estimation, manifold learning, hidden Markov models, conditional random fields, and random forests. The lecture is accompanied by exercises, where theoretical results are practically implemented and applied.
 
Have you ever wondered about the mysterious "collapse of the wave function" or the "wave-particle duality"? Does Schrödinger´s cat make you uneasy? Do you have a feeling that there could be a deeper, more ´microscopic´ theory underlying Quantum Mechanics? Do you believe that trajectories are ruled out by Quantum Mechanics? Are you confused by the concept of spin or by fermions vs. bosons? Do you want to learn how the founding fathers´ "Gedankenexperiments" are now routinely realized in the l ...
 
More information aboutnbsp;Machine Learning for Physicists: https://machine-learning-for-physicists.org
 
Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises: (multilayer) perceptron, backpropagation, fully connected neural networks loss functions and optimization strategies convolutional neural networks (CNNs) activation functions regularization strategies common practic ...
 
The Nuremberg Moot Court (nuremberg-moot.de) takes place every year. The competition aimes to encourage university students to become familiar with International Criminal Law by arguing a fictitious case in front of the "International Criminal Court". At the same time, it promotes the fundamental heritage of the Nuremberg Trials: criminal accountability within armed conflicts in keeping with the spirit of Human Rights and governed by the principle of fair trial as enshrined in the ICCPR in p ...
 
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