Winter Term 2024/2025

1300232210
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1300232211
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1300232215
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Content

-- CANCELLED FOR WINTER TERM 2024/25 -- 
The seminar is organized as journal club.

Goal

The participants will select a paper given in the Materials selection below and present the content to the group followed by a discussion.  Afterwards, the paper is summerized in a report.

1300272106
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Content

Infos auf unserer Website: https://sciai-lab.org/teaching/24w/mlph/

1300282202
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Content

- Basic concepts of numerical simulations, continuous and discrete simulations
- Discretization or ordinary differential equations, integration schemes of different order
- N-Body problems, molecular dynamics, collisionless systems
- Discretization of partial differential equations
- Finite element and finite volume methods
- Lattice methods
- Adaptive mesh refinement and multi-grid methods
- Matrix solvers and FFT methods
- Monte Carlo methods, Markov Chains, applications in statistical physics

Goal

After completion of this module, the students are endowed with the capacity to identify and classify numerical problems. They have reached active understanding of applicable numerical methods and algorithms. They are able to solve basic physical problems with adequate numerical techniques and to recognize the range of validity of numerical solutions.

1300282203
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Content

https://www.h-its.org/teachings/ws-2024-25-lectures-and-hands-on-sessions-in-computational-molecular-biophysics/ 

Only a limited number of students can be accepted. Please register in advance by sending an Email to Frauke.Graeter@h-its.org.

1300282207
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Content

Please find all information here:

https://hci.iwr.uni-heidelberg.de/content/computer-vision

*** Formalities:

Teaching assistants (main point of contact): Friedrich Feiden: johann-friedrich.feiden@iwr.uni-heidelberg.de; Zavadski, Denis: denis.zavadski@iwr.uni-heidelberg.de

Registration: Moodle: https://moodle.uni-heidelberg.de/course/view.php?id=24146


Prerequisite: no prerequisites, but it is recommended to have Machine Learning Background, e.g. Fundamentals of Machine Learning or equivalent

Exam: Either oral exam or mini-project (as in last years).
Leistungspunkte: 6 LP
Usability: Physics, MSc., Angewandte Informatik, MSc. Scientific Computing

Goal

- Brief Introduction to necessary Machine Learning (incl. U-Net, ResNet, Vision Transformers) - Basic Image Processing (incl. linear/non-linear Filtering) - Sparse feature Detection and Description (incl. SIFT and LIFT) - Projective Geometry, Epipolar Geometry - Sparse 3D Reconstruction , SLAM and Camera Localization - Neural Randiance Fields (NERF) - Robust Matching (incl. Differentiable RANSAC) - Object Tracking (incl. Particle Filter, Kalman Filter, ) - Object Recognition - Image Generation (incl. GAN, Diffusion, VAE, Flows) - Training Data Generation

1300282271
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Content

https://drive.google.com/file/d/1Zaorp0tHFz-oyY6wDrJ2xseTx_3fyi50/view

1300282212
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Content

https://www.quanty.org/workshop/heidelberg/september_2024/programme

This is an international summer school accepting 40 participants. Please register at https://www.quanty.org/workshop/heidelberg/september_2024/registration lecture goes one week from 23.9 to 28.9 2024