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--- mathematical_methods_machine_learning [2025/02/17 08:55] – [Weeks 9 & 10 (2025)] Diaaeldin Taha
+++ mathematical_methods_machine_learning [2025/03/13 13:05] (current) – [Schedule] Diaaeldin Taha
@@ Line 21: / Line 21: @@
 ^ 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 50 (2024) | Mon, 09.12.2024 | ---  | ---      | ---           | **NO MEETING**   |
 ^ 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, A3 01      | Jan Ewald             | Autoencoder and Their Variants for Biomedical Data   |
@@ Line 28: / Line 29: @@
 ^ Week 7 (2025)  | Mon, 10.02.2025 | 14:00–15:30  | MIS, A3 01      | Guido Montufar        | An Overview of Theories for Feature Learning in Neural Networks I   |
 ^ Week 8 (2025)  | Mon, 17.02.2025 | 14:00–15:30  | MIS, A3 01      | Guido Montufar        | An Overview of Theories for Feature Learning in Neural Networks II   |
-^ Week 9 (2025)  | Mon, 24.02.2025 | 14:00–15:30  | MIS, A3 01      | Paul Breiding         | Computing with Varieties I   |
+^ Week 9 (2025)  | Mon, 24.02.2025 | 14:00–15:30  | MIS, A3 01      | Paul Breiding         | Computing with Algebraic Varieties I   |
-^ Week 10 (2025) | Mon, 03.03.2025 | 14:00–15:30  | MIS, A3 01      | Paul Breiding         | Computing with Varieties II   |
+^ Week 10 (2025) | Mon, 03.03.2025 | ---  | ---     | ---         | **NO MEETING**   |
-^ Week 11 (2025) | Mon, 10.03.2025 | 14:00–15:30  | MIS, A3 01      | Angelica Torres       | Varieties in Machine Learning I   |
+^ Week 11 (2025) | Mon, 10.03.2025 | ---  | ---      | ---         | **NO MEETING**   |
-^ Week 12 (2025) | Mon, 17.03.2025 | 14:00–15:30  | MIS, A3 01      | Angelica Torres       | Varieties in Machine Learning II   |
+^ Week 12 (2025) | Mon, 17.03.2025 | 14:00–15:30  | MIS, A3 01      | Angelica Torres       | Varieties in Machine Learning I   |
-^ Week 13 (2025) | Mon, 24.03.2025 | 14:00–15:30  | MIS, A3 01      | Marzieh Eidi          | Geometric Machine Learning   |
+^ Week 13 (2025) | Mon, 24.03.2025 | 14:00–15:30  | MIS, A3 01      | Angelica Torres       | Varieties in Machine Learning II   |
+^ Week 11 (2025) | Mon, 31.03.2025 | ---  | ---      | ---         | **NO MEETING**   |
+^ Week 14 (2025)  | Mon, 07.04.2025 | 14:00–15:30  | MIS, A3 01      | Paul Breiding         | Computing with Algebraic Varieties II   |
+^ Week 15 (2025) | Mon, 14.04.2025 | 14:00–15:30  | MIS, A3 01      | Marzieh Eidi          | Geometric Machine Learning   |
 ===== Information =====
@@ Line 105: / Line 109: @@
 **Description**: Feature learning, or learning meaningful representations of raw data, has been one of the guiding ideas behind deep learning. In principle and in practice deep neural networks can automatically learn hierarchies of data representations which successively distill the relevant parts of a problem and make it easier to arrive at a good solution. However, developing a theoretical framework to characterize feature learning and the properties of the data representations that are learned by a neural network is still an ongoing program. These lectures discuss some of the recent perspectives and advances in this direction.
-==== Weeks 9 & 10 (2025) ====
+==== Weeks 9 & 11 (2025) ====
 **Speaker**: Paul Breiding
@@ Line 112: / Line 116: @@
 **Description**: These lectures will consider algebraic varieties from a computational point of view.  They will cover topics such as dimension, degree, Gröbner bases and homotopy continuation. As a motivation, we will discuss varieties relevant in machine learning and data science.
-==== Weeks 11 & 12 (2025) ====
+==== Weeks 12 & 13 (2025) ====
-**Speaker**: Angelica Torres
+**Speaker**: Angelica Torres (MPI MIS)
-==== Weeks 13 (2025) ====
+**Title**: Varieties in Machine Learning I & II
+**Description**:
+  * **Varieties in Machine Learning I**: In this session we will build on Paul Breiding’s lectures and focus on the relation between the implicitization problem in Algebraic Geometry and the function space of neural networks.  We will review the work by Kohn, Trager, Kileel, Montufar, Li, and others, regarding the geometric properties of the function space of linear neural networks and polynomial neural networks.
+  * **Varieties in Machine Learning II**: In this session we focus on the algebraic and geometric properties of the varieties studied in the previous session. We will pay special attention to the Euclidean Distance Degree and its relation with the optimization problem in Machine Learning.
+==== Weeks 14 (2025) ====
 **Speaker**: Marzieh Eidi