392151 Probabilistic Learning for Visual Computing (S) (SoSe 2024)

This course offers a probabilistic perspective to machine learning, applied to visual computing applications. A central theme is the probabilistic inference and probabilistic modeling of images, human pose and motion, and 3D shapes and appearances. Topics include
* classification (recap),
* probabilistic regression,
* factorized representations for human pose estimation, and
* generative models, such as variational autoencoders (VAE), generative adversarial networks (GANs), and diffusion models (DDIM & DDPM, classifier free and classifier-based guidance).

The students will present recent papers on computer graphics and computer vision that have a strong probabilistic component. Practical experiences for applying probabilistic models in PyTorch are gained in the assignments and tutorials.

The course is largely based on the Visual AI course I gave at UBC: https://www.cs.ubc.ca/~rhodin/2022_2023_CPSC_533R/

The following slides provide additional details and context to the existing visual computing modules at Bielefeld University:
https://docs.google.com/presentation/d/e/2PACX-1vRGyE0j_cU324qp-vOEE4hEBPn3HdwliZQqeg_vT0w7Yt9EUXmgFB6NamL_ynqeBMYRgD-cdvfvXobA/pub?start=false&loop=false&delayms=3000

Requirements for participation, required level

Required skills: Excellent Python programming skills (>1 year), fundamental mathematics, and basics of machine learning or neural networks.
Optional skills: PyTorch or TensorFlow experience, computer graphics, computer vision (lectures or projects).

Students need not be experts in all aspects of visual computing but have a strong background in one of graphics, vision, and machine learning. These skills are typically acquired through lectures or by private projects, hackathons, and coding competitions.

Bibliography

The papers that will be discussed are from the CVPR, SIGGRAPH, and NeurIPS conferences, or other respected computer vision and graphics venues.

The (online) book Probabilistic Machine Learning - An Introduction by Kevin P. Murphy serves as the basis for the lectures:
https://probml.github.io/pml-book/book1.html

A few chapters from the follow-up book Probabilistic Machine Learning - Advanced topics by Kevin P. Murphy are relevant too:
https://probml.github.io/pml-book/book2.html