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| Semester | Herbstsemester 2024 |
| Angebotsmuster | unregelmässig |
| Dozierende | Aurelien Lucchi (aurelien.lucchi@unibas.ch, BeurteilerIn) |
| Inhalt | The class focuses on the theoretical concepts behind Deep learning. We will discuss the following concepts: General introduction to linear networks, activations, etc Approximation Theory Complexity Theory Network Architectures Optimization Optimization Landscape of Neural Networks Neural Tangent Kernel Regularization Generalization bounds Adversarial examples |
| Lernziele | The main goal is to understand the theoretical foundations of Deep Learning. This includes the following important concepts: - Universal approximation: can a neural network approximate any arbitrary function? - Optimization: how do we optimize the parameters of a neural network? what theoretical guarantees do we have about finding a good solution? - Generalization: under what conditions does the solution of a neural network generalizes to unseen data? - Adversarial robustness: how robust is a neural network to adversarial attacks? |
| Bemerkungen | Exercise sessions will start the second week of the semester. |
| Teilnahmebedingungen | - Machine Learning (classification, regression, kernels, etc) - Linear algebra - Calculus, Basic concepts in topology - Probability theory (random variable, expectation, density, etc) - Some non-mandatory exercises will require coding in python (reasonable coding skills in another programming language should be sufficient to learn python) Note that a significant part of the class focuses on understanding theoretical aspects, we will thus be covering proofs that require a good knowledge of the mathematical concepts discussed above. |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| wöchentlich | Montag | 10.15-12.00 | Bernoullistrasse 30/32, Hörsaal 103 |
| wöchentlich | Donnerstag | 16.15-18.00 | Spiegelgasse 5, Seminarraum 05.001 |
| Datum | Zeit | Raum |
|---|---|---|
| Montag 16.09.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 19.09.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 23.09.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 26.09.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 30.09.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 03.10.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 07.10.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 10.10.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 14.10.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 17.10.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 21.10.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 24.10.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 28.10.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 31.10.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 04.11.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 07.11.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 11.11.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 14.11.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 18.11.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 21.11.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 25.11.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 28.11.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 02.12.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 05.12.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 09.12.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 12.12.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Montag 16.12.2024 | 10.15-12.00 Uhr | Bernoullistrasse 30/32, Hörsaal 103 |
| Donnerstag 19.12.2024 | 16.15-18.00 Uhr | Spiegelgasse 5, Seminarraum 05.001 |
| Module |
Doktorat Informatik: Empfehlungen (Promotionsfach: Informatik) Modul: Applications of Distributed Systems (Masterstudium: Computer Science) Modul: Applications of Machine Intelligence (Masterstudium: Computer Science) Modul: Concepts of Machine Intelligence (Masterstudium: Computer Science) Modul: Electives in Data Science (Masterstudium: Data Science) Modul: Machine Learning Foundations (Masterstudium: Data Science) Modul: Methods of Machine Intelligence (Masterstudium: Computer Science) |
| Leistungsüberprüfung | Lehrveranst.-begleitend |
| Hinweise zur Leistungsüberprüfung | Continuous assessment Note the following split: 15% continuous assesment (short exercises and Q&As given in class) 20% homework 30% project (writeup and presentation) 35% written exam A 50% score on HW sets is required to participate in the final exam. A score of 3 out of 6 is required at the exam to pass the class. Expected date: Thursday, February 1, 2024, 10-12 a.m. |
| An-/Abmeldung zur Leistungsüberprüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,5 |
| Wiederholtes Belegen | beliebig wiederholbar |
| Zuständige Fakultät | Philosophisch-Naturwissenschaftliche Fakultät, studiendekanat-philnat@unibas.ch |
| Anbietende Organisationseinheit | Fachbereich Informatik |