====== Math and Machine Learning: Theory and Applications (Fall 2024) ======

===== Registration =====

  * Register on the [[https://www.mis.mpg.de/events/event/mathematics-and-machine-learning|MPI course website]].

===== Organization =====

  * **Location**: [[https://www.mis.mpg.de/|Max Planck Insitute for Mathematics in the Sciences]], Seminar Room E2 10 (Leon Lichtenstein)
  * **Organizers**: Parvaneh Joharinad, Diaaeldin Taha
  * **Institutional Website**: [[https://www.mis.mpg.de/events/event/mathematics-and-machine-learning|link]]
  * **Contact**: To contact the organizers, email the lab at ''lab [at] mis [dot] mpg [dot] de''.
  * **Mailing List**: To stay informed of Lab activities, including this group's meetings, join the [[https://listserv.mis.mpg.de/listinfo/lab-newsletter|Lab mailing list]].

===== Schedule =====

^ Week        ^ Date          ^ Time         ^ Location   ^ Speaker              ^ Topic ^
^ Week 45 (2024) | Mon, 04.11.2024 | 15:00–16:30  | MIS, E2 10      | Diaaeldin Taha | Graph and Topological Neural Networks I   |
^ Week 46 (2024) | Mon, 11.11.2024 | 15:00–16:30  | MIS, E2 10      | Diaaeldin Taha | Graph and Topological Neural Networks II   |
^ Week 47 (2024) | Mon, 18.11.2024 | 15:00–16:30  | MIS, E2 10      | Parvaneh Joharinad | Group Equivariant Neural Networks I   |
^ Week 48 (2024) | Mon, 25.11.2024 | 15:00–16:30  | MIS, E2 10      | Parvaneh Joharinad | Group Equivariant Neural Networks II   |
^ Week 49 (2024) | Mon, 02.12.2024 | 15:00–16:30  | MIS, G3 10      | Nico Scherf           | Deep Generative Models   |
^ Week 51 (2024) | Mon, 16.12.2024 | 15:00–16:30  | MIS, E2 10      | Jan Ewald             | On the (Underestimated) Importance of Objective/Loss Functions   |
^ Week 3 (2025)  | Mon, 13.01.2025 | 14:00–15:30  | MIS, E2 10      | Jan Ewald             | Autoencoder and Their Variants for Biomedical Data   |
^ Week 4 (2025)  | Mon, 20.01.2025 | 14:00–15:30  | MIS, E2 10      | Duc Luu               | Learning Dynamical Systems I   |
^ Week 5 (2025)  | Mon, 27.01.2025 | 14:00–15:30  | MIS, E2 10      | Duc Luu               | Learning Dynamical Systems II   |
^ Week 6 (2025)  | Mon, 03.02.2025 | 14:00–15:30  | MIS, E2 10      | Robert Haase          | Large Language Models for Code Generation   |
^ Week 7 (2025)  | Mon, 10.02.2025 | 14:00–15:30  | MIS, E2 10      | Guido Montufar        | Foundations of Feature Learning I   |
^ Week 8 (2025)  | Mon, 17.02.2025 | 14:00–15:30  | MIS, E2 10      | Guido Montufar        | Foundations of Feature Learning II   |
^ Week 9 (2025)  | Mon, 24.02.2025 | 14:00–15:30  | MIS, E2 10      | Paul Breiding         | Computing with Varieties I   |
^ Week 10 (2025) | Mon, 03.03.2025 | 14:00–15:30  | MIS, E2 10      | Paul Breiding         | Computing with Varieties II   |
^ Week 11 (2025) | Mon, 10.03.2025 | 14:00–15:30  | MIS, E2 10      | Angelica Torres       | Varieties in Machine Learning I   |
^ Week 12 (2025) | Mon, 17.03.2025 | 14:00–15:30  | MIS, E2 10      | Angelica Torres       | Varieties in Machine Learning II   |
^ Week 13 (2025) | Mon, 24.03.2025 | 14:00–15:30  | MIS, E2 10      | Marzieh Eidi          | Geometric Machine Learning   |

===== Information =====

==== Weeks 45 & 46 (2024) ====

**Speaker**: Diaaeldin Taha (Max Planck Institute for Mathematics in the Science, Germany)

**Description**: In these two sessions, we will provide an overview of deep learning with a focus on graph and topological neural networks. We will begin by reviewing neural networks, parameter estimation, and the universal approximation theorem. Then, we will discuss graphs and motivate graph convolutional neural networks by tracing their origins from spectral filters in signal processing. Lastly, we will review recent progress in topological deep learning, particularly focusing on simplicial, cellular, and hypergraph neural networks as extensions of graph neural networks. We will assume a basic familiarity with linear algebra and calculus; all relevant concepts from graph theory and topology will be introduced.

**References**:
  * Bodnar, C., Frasca, F., Otter, N., Wang, Y., Lio, P., Montufar, G. F., & Bronstein, M. (2021). Weisfeiler and lehman go cellular: Cw networks. Advances in Neural Information Processing Systems.
  * Bodnar, C., Frasca, F., Wang, Y., Otter, N., Montufar, G. F., Lio, P., & Bronstein, M. (2021). Weisfeiler and lehman go topological: Message passing simplicial networks. In International Conference on Machine Learning. PMLR.
  * Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
  * Hajij, M., Papamarkou, T., Zamzmi, G., Natesan Ramamurthy, K., Birdal, T., & Schaub, M. T. (2024). Topological deep learning: Going beyond graph data. Published online: https://tdlbook.org/
  * Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations. PMLR.

==== Weeks 47 & 48 (2024) ====

**Speaker**: Parvaneh Joharinad

==== Week 49 (2024) ====

**Speaker**: Nico Scherf

==== Weeks 50 (2024) & 3 (2025) ====

**Speaker**: Jan Ewald

==== Weeks 4 & 5 (2025) ====

**Speaker**: Duc Luu

==== Weeks 6 (2025) ====

**Speaker**: Robert Hasse

==== Weeks 7 & 8 (2025) ====

**Speaker**: Guido Montufar

==== Weeks 9 & 10 (2025) ====

**Speaker**: Paul Breiding

==== Weeks 11 & 12 (2025) ====

**Speaker**: Angelica Torres

==== Weeks 13 (2025) ====

**Speaker**: Marzieh Eidi