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Deutsch Intern
  • [Translate to Englisch:] Studierende im Hörsaal während einer Vorlesung
Mathematics of Machine Learning

Courses

Current Courses: Summer Semester 2024

Machine Learning with Graphs (Master Seminar)

Lecturers: Prof. Dr. Leon Bungert, Dr. Eloi Martinet

In this seminar we will discover machine learning methods that involve graphs. This includes partial differential equations on graphs and their use for semi-supervised machine learning, as well as graph neural networks for supervised learning with graph data. The seminar will cover theoretical and numerical aspects and can lead to a master's thesis in this topic.

For any questions, please feel free to contact leon.bungert@uni-wuerzburg.de. I look forward to your interest!

Wed 10-12  S0. 101

 

References:

[1] Calder, J., Cook, B., Thorpe, M., & Slepcev, D. (2020, November). Poisson learning: Graph based semi-supervised learning at very low label rates. In International Conference on Machine Learning (pp. 1306-1316). PMLR.

[2] Calder, J. (2018). The game theoretic p-Laplacian and semi-supervised learning with few labels. Nonlinearity, 32(1), 301.

[3] Bungert, L., Calder, J., & Roith, T. (2023). Uniform convergence rates for Lipschitz learning on graphs. IMA Journal of Numerical Analysis, 43(4), 2445-2495.

[4] Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017). Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 34(4), 18-42.

[5] Bronstein, M. M., Bruna, J., Cohen, T., & Veličković, P. (2021). Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv preprint arXiv:2104.13478.

[6] Xia, F., Sun, K., Yu, S., Aziz, A., Wan, L., Pan, S., & Liu, H. (2021). Graph learning: A survey. IEEE Transactions on Artificial Intelligence, 2(2), 109-127.

[7] Song, Z., Yang, X., Xu, Z., & King, I. (2022). Graph-based semi-supervised learning: A comprehensive review. IEEE Transactions on Neural Networks and Learning Systems.

Mathematical Foundations of Data Science II (2+1 in the Bachelor program Mathematical Data Science)

Lecturer: Prof. Leon Bungert

The lecture "Mathematical Foundations of Data Science" covers the essential mathematical concepts that are crucial for understanding and applying Data Science and machine learning. It includes introductions to fundamental mathematical concepts of linear algebra and statistics. Additionally, specific mathematical techniques and methods used in data analysis and machine learning are presented. These include optimization, numerical methods, linear regression, cluster analysis, dimensionality reduction, artificial neural networks, and deep learning.

The lecture focuses on teaching students the fundamental methods and concepts of Data Science and how to apply them in practical scenarios. For any questions, please feel free to contact leon.bungert@uni-wuerzburg.de. I look forward to your interest!

Tue 10-12 

Übung zu Mathematical Foundations of Data Science II

Mon 12-14

The rooms will be announced later.

Numerical Mathematics and Applied Analysis (Working Group)

Lecturer: Dr. Eloi Martinet

Mon 16-18 S0 102, Wed 8-10 S0. 107

For any questions, please feel free to contact eloi.martinet@uni-wuerzburg.de. I look forward to your interest!

Former Courses

  • Mathematical Foundations of Data Science

Lecturer: Prof. Leon Bungert