Lehre
Matrix Methods in Data Science: Decompositions, Tensors and Beyond (Forschungsmodul)
Lectures: Prof. Dr. Martin Stoll
Exercises: Prof. Dr. Martin Stoll
Aktuelles & allgemeine Hinweise
- Prerequisites: Introduction to analysis, linear algebra, and numerical analysis.
- Contents: Motivating examples, Matrix factorisations for Classification and Learning: QR, SVD, Randomization, Nonnegative Matrix Factorisation, Numerical Tensor Methods, ...
- OPAL Einschreibung
- Typische Pruefungsfragen!
Handouts und Misc
Vorlesungstermine
- Di, 11-12.30 Uhr einmalig 2/B202
- Di, 2. LE, 2/B202
- Do, 3. LE, 2/B202
Übungstermine
- Do, 3. LE, 39/138 (26.4.2018, 17.5.2018)
Übungsblätter
- The exercises will be taken as labs, in the sense that I will beforehand state, which parts of the lectures will be needed, and students will be given a programming task at the beginning of the exercise.
| Besprechung | Bemerkungen |
|---|---|
| 26.4. | Learn how to clone a directory from GitHub! Recall the discussin of the SVD from the lectures. |
| 17.5. | Apply the CUR factorisation to an image problem and supreme court data. |
Prüfung
Literatur
- Eldén, Lars; Matrix methods in data mining and pattern recognition
- Kolda, Tamara G; Bader, Brett W; Tensor decompositions and applications SIAM review
- Von Luxburg, Ulrike; A tutorial on spectral clustering Statistics and computing
- Sorensen, Danny C; Embree, Mark; A deim induced cur factorization SIAM Journal on Scientific Computing
- Higham, Catherine F; Higham, Desmond J; Deep Learning: An Introduction for Applied Mathematicians arXiv preprint arXiv:1801.05894
- Gillis, Nicolas; The why and how of nonnegative matrix factorization Regularization, Optimization, Kernels, and Support Vector Machines