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66937-01 - Lecture with practical courses: Foundations of Deep Learning (6 CP)
| Semester | fall semester 2026 |
| Course frequency | Irregular |
| Lecturers | Aurelien Lucchi (aurelien.lucchi@unibas.ch, Assessor) |
| Content | 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 |
| Learning objectives | 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? |
| Comments | Exercise sessions will start the second week of the semester. |
| Admission requirements | - 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. |
| Language of instruction | German |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Wednesday | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| wöchentlich | Thursday | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
Dates
| Date | Time | Room |
|---|---|---|
| Wednesday 16.09.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 17.09.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 23.09.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 24.09.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 30.09.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 01.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 07.10.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 08.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 14.10.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 15.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 21.10.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 22.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 28.10.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 29.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 04.11.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 05.11.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 11.11.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 12.11.2026 | 16.15-18.00 | --, -- |
| Wednesday 18.11.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 19.11.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 25.11.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 26.11.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 02.12.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 03.12.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 09.12.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 10.12.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 16.12.2026 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 17.12.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Modules |
Doctorate Computer Science: Recommendations (PhD subject: Computer Science) Module: Applications of Distributed Systems (Master's Studies: Computer Science (Start of studies before 01.08.2026)) Module: Applications of Machine Intelligence (Master's Studies: Computer Science (Start of studies before 01.08.2026)) Module: Concepts of Machine Intelligence (Master's Studies: Computer Science (Start of studies before 01.08.2026)) Module: Electives in Data Science (Master's Studies: Data Science) Module: Machine Intelligence (Master's Studies: Computer Science) Module: Machine Intelligence (Master's degree subject: Computer Science) Module: Machine Learning Foundations (Master's Studies: Data Science) Module: Methods of Machine Intelligence (Master's Studies: Computer Science (Start of studies before 01.08.2026)) |
| Assessment format | continuous assessment |
| Assessment details | Continuous assessment Note the following split: 15% continuous assesment (short exercises and Q&As given in class) 25% mid-term exam (written). Date: during exercise session on Thursday, 30.10.2025 25% project (writeup and presentation) 35% final exam (written) Expected date final exam: Thursday, February 4, 2027, 10-12 a.m. |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,5 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Science, studiendekanat-philnat@unibas.ch |
| Offered by | Fachbereich Informatik |