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66937-01 - Lecture with practical courses: Foundations of Deep Learning (6 CP)
| Semester | fall semester 2025 |
| 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 | English |
| 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 17.09.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 18.09.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 24.09.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 25.09.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 01.10.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 02.10.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 08.10.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 09.10.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 15.10.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 16.10.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 22.10.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 23.10.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 29.10.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 30.10.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 05.11.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.001 |
| Thursday 06.11.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 12.11.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 13.11.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 19.11.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 20.11.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 26.11.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 27.11.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 03.12.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Thursday 04.12.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 115 |
| Wednesday 10.12.2025 | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Modules |
Doctorate Computer Science: Recommendations (PhD subject: Computer Science) Module: Applications of Distributed Systems (Master's Studies: Computer Science) Module: Applications of Machine Intelligence (Master's Studies: Computer Science) Module: Concepts of Machine Intelligence (Master's Studies: Computer Science) Module: Electives in Data Science (Master's Studies: Data Science) Module: Machine Learning Foundations (Master's Studies: Data Science) Module: Methods of Machine Intelligence (Master's Studies: Computer Science) |
| 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 5, 2026, 10-12 a.m., Spiegelgasse 5, room 05.002. |
| 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 |