Welcome to FYS5429/9429

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. 

The first three weeks will focus on neural networks and their mathematics, since they function as the building blocks in many other deep learning methods.

The course can also be used as a self-study course and besides the lectures, many of you have worked independently on own projects. In general, in addition to the lectures, we have often followed five main paths:

1) Projects and exercises that follow the lectures

2) The coding path. This leads often to a single project only where one focuses on coding for example CNNs or RNNs or parts of LLMs from scratch.

3) The Physics Informed neural network path (PINNs). Here we define some basic PDEs which are solved by using PINNs

4) The own data path. Some of you may have data you wish to analyze with different deep learning methods

5) This path, the Bayesian ML path is not covered by the present lecture material and leads normally to independent self-study work.

Best wishes to you all and don't hesitate to get in touch with us.

Edvin and Morten