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66096-01 - Lecture with practical courses: Mathematics of Data Science (8 CP)
| Semester | fall semester 2026 |
| Course frequency | Every fall sem. |
| Lecturers |
Foivos Alimisis (foivos.alimisis@unibas.ch)
Lorenzo Baldassari (lorenzo.baldassari@unibas.ch) Aurelien Lucchi (aurelien.lucchi@unibas.ch, Assessor) |
| Content | This course provides an introduction to modern probability theory and its applications in high-dimensional data analysis. We begin with the fundamentals of probability, conditioning, and independence, then study limiting phenomena and concentration inequalities. Building on these foundations, the course explores random vectors, high-dimensional geometry, and random matrix theory, with applications to understanding spectral properties, the Marchenko–Pastur law, and double descent phenomena. Advanced topics include matrix concentration bounds, functional calculus, and stochastic processes. The course combines theory with exercises and a mid-term exam to reinforce understanding of these topics. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Course auditors welcome |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Monday | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| wöchentlich | Tuesday | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| wöchentlich | Thursday | 08.15-10.00 | Kollegienhaus, Hörsaal 120 |
Dates
| Date | Time | Room |
|---|---|---|
| Monday 14.09.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 15.09.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| Thursday 17.09.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 119 |
| Monday 21.09.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 22.09.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| Thursday 24.09.2026 | 08.15-10.00 | Alte Universität, Seminarraum -201 |
| Monday 28.09.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 29.09.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| Thursday 01.10.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 119 |
| Monday 05.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 06.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| Thursday 08.10.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 120 |
| Monday 12.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 13.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| Thursday 15.10.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 120 |
| Monday 19.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 20.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| Thursday 22.10.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 120 |
| Monday 26.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 27.10.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| Thursday 29.10.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 120 |
| Monday 02.11.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 03.11.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| Thursday 05.11.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 120 |
| Monday 09.11.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 10.11.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| Thursday 12.11.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 120 |
| Monday 16.11.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 17.11.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| Thursday 19.11.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 120 |
| Monday 23.11.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 24.11.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 118 |
| Thursday 26.11.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 120 |
| Monday 30.11.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 01.12.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| Thursday 03.12.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 120 |
| Monday 07.12.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 08.12.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 120 |
| Thursday 10.12.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 120 |
| Monday 14.12.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 116 |
| Tuesday 15.12.2026 | 16.15-18.00 | Kollegienhaus, Hörsaal 114 |
| Thursday 17.12.2026 | 08.15-10.00 | Kollegienhaus, Hörsaal 120 |
| Monday 11.01.2027 | 16.15-18.00 | Alte Universität, Hörsaal -101 |
| Modules |
Module: Data Engineering (Master's Studies: Computer Science) Module: Data Engineering (Master's degree subject: Computer Science) Module: Machine Intelligence (Master's Studies: Computer Science) Module: Machine Intelligence (Master's degree subject: Computer Science) Module: Mathematical Foundations (Master's Studies: Data Science) |
| Assessment format | continuous assessment |
| Assessment details | Mid-term exam on November 5th, 2027 Information regarding the assessment: Continuous assessment 20% Short exercises in class (15min)*6 Mid-term exam 35% Final exam 45% Final exam on January 11th, 2027, 4.15-6pm (Alte Universität, Hörsaal -101) |
| 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 | Departement Mathematik und Informatik |