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| Semester | spring semester 2021 |
| Course frequency | Irregular |
| Lecturers | Ivan Dokmanić (ivan.dokmanic@unibas.ch, Assessor) |
| Content | - High-dimensional probability for machine learning and data science - Nonlinear dimensionality reduction (manifold learning, graph-based methods) - Learning on graphs; graph neural networks; learning with invariances and equivariances - Applications in scientific machine learning |
| Learning objectives | - Understand the idea of the concentration of measure - Get an intuition for curses and blessings of dimensionality - Understand the central role of low-dimensional structures (manifolds, sparsity, ...) and means to learn them - Understand how to model data using graphs - Understand the principles behind graph neural networks |
| Bibliography | Will be announced in the lecture. |
| Admission requirements | Successful completion of introductory math courses. Fundamentals of linear algebra, probability, and stats. Understanding of scientific computing and pattern recognition. Coding in Python. If you are unsure whether this courses is for you please contact the teacher. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Course auditors welcome |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Monday | 15.15-17.00 | - Online Präsenz - |
| Date | Time | Room |
|---|---|---|
| Monday 01.03.2021 | 15.15-17.00 | - Online Präsenz -, -- |
| Monday 08.03.2021 | 15.15-17.00 | - Online Präsenz -, -- |
| Monday 15.03.2021 | 15.15-17.00 | - Online Präsenz -, -- |
| Monday 22.03.2021 | 15.15-17.00 | - Online Präsenz -, -- |
| Monday 29.03.2021 | 15.15-17.00 | - Online Präsenz -, -- |
| Monday 05.04.2021 | 15.15-17.00 | Ostern |
| Monday 12.04.2021 | 15.15-17.00 | - Online Präsenz -, -- |
| Monday 19.04.2021 | 15.15-17.00 | - Online Präsenz -, -- |
| Monday 26.04.2021 | 15.15-17.00 | - Online Präsenz -, -- |
| Monday 03.05.2021 | 15.15-17.00 | - Online Präsenz -, -- |
| Monday 10.05.2021 | 15.15-17.00 | - Online Präsenz -, -- |
| Monday 17.05.2021 | 15.15-17.00 | - Online Präsenz -, -- |
| Monday 24.05.2021 | 15.15-17.00 | Pfingstmontag |
| Monday 31.05.2021 | 15.15-17.00 | - Online Präsenz -, -- |
| Monday 07.06.2021 | 13.00-15.00 | Bernoullianum, Grosser Hörsaal 148 |
| Modules |
Doctorate Computer Science: Recommendations (PhD subject: Computer Science) Modul: Concepts of Machine Intelligence (Master's degree subject: Computer Science) Module Specialisation: Applied Mathematics (Master's Studies: Mathematics) Module: Applications of Distributed Systems (Master's Studies: Computer Science) Module: Applications of Machine Intelligence (Master's Studies: Computer Science) Module: Methods of Machine Intelligence (Master's Studies: Computer Science) |
| Assessment format | continuous assessment |
| Assessment details | 30% homework 40% project (writeup and presentation) 30% written exam A 50% score on HW sets is required to participate in the final exam. Expected Date of the written exam: 7 June 2021, 1-3 p.m., Bernoullianum, Grosser Hörsaal 148 |
| 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 |