Semester page for FYS5429 - Spring 2025

Dear all, we hope this week has started the best possible way. Here are our plans for the lecture on January 30.

The aim this week is to give a review of the basics of neural networks. Many of you have seen similar material before, but we think it is useful to repeat some of the basics as neural networks are essential parts of most algorithms we describe later, whether these are CNNs, RNNs, autoencoders or other methods we will discuss.

We will also present and discuss different project variants. Next week we will also try to have presentations from those of you who have defined specific and own projects.

Note also that I have changed the zoom link to my UiO account. Our permanent zoom link for the rest of the semester is

https://uio.zoom.us/my/mortenhj

The jupyter-notebook  with the material for this week (with code examples) is at ...

Our zoom link for the rest of the semester is https://uio.zoom.us/my/mortenhj

Dear all, welcome to FYS5429/9429. Our first lecture is Thursday January 23 at 1215pm to 2pm. We have also set aside an eventual lab session from 2pm to 4pm on Thursdays. Our lecture room is F?434 at the Department of Physics.   For those who cannot attend in person, the zoom link is https://msu.zoom.us/j/99649445421 Meeting ID: 996 4944 5421 and all lectures will be recorded.  

The emphasis is on deep learning algorithms, starting with the mathematics of neural networks (NNs), moving on to convolutional NNs (CNNs) and recurrent NNs (RNNs), autoencoders, graph neural networks and other dimensionality reduction methods to finally discuss generative methods. These will include Boltzmann machines, variational autoencoders, generalized adversarial networks, diffusion methods and other. See the course GitHub link for more information, weekly plans and more http...

Dear all, welcome to FYS5429/9429. Our first lecture is Thursday January 23 at 1215pm to 2pm. We have also set aside an eventual lab session from 2pm to 3pm on Thursdays. Our lecture room is F?434 at the Department of Physics. 

The emphasis is on deep learning algorithms, starting with the mathematics of neural networks (NNs), moving on to convolutional NNs (CNNs) and recurrent NNs (RNNs), autoencoders, graph neural networks and other dimensionality reduction methods to finally discuss generative methods. These will include Boltzmann machines, variational autoencoders, generalized adversarial networks, diffusion methods and other. See the course GitHub link for more information, weekly plans and more https://github.com/CompPhysics/AdvancedMachineLearning/tree/main. There you will also find a tentative weekly plan with lecture notes and reading suggestions. You will also find a link to the textbooks (with codes and more) that we will follow.