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@@ -5,7 +5,7 @@ Analysis and Machine Learning, spanning from weekly plans to lecture
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material and various reading assignments. The emphasis is on deep
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learning algorithms, starting with the mathematics of neural networks
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(NNs), moving on to convolutional NNs (CNNs) and recurrent NNs (RNNs),
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autoencoders, graph neural networks and other dimensionality reduction methods to finally
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autoencoders, transformers, graph neural networks and other dimensionality reduction methods to finally
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discuss generative methods. These will include Boltzmann machines,
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variational autoencoders, generalized adversarial networks, diffusion methods and other.
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@@ -37,7 +37,7 @@ variational autoencoders, generalized adversarial networks, diffusion methods an
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- Generative Adversarial Networks (GANs)
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- Autoregressive methods (tentative)
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### Physical Sciences (often just called Physics informed) informed machine learning
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- Basic set up of PINNs with discussion of projects
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FYS5429 zoom link to be announced when semester starts
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