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| Semester | spring semester 2022 |
| Course frequency | Irregular |
| Lecturers | Aurelien Lucchi (aurelien.lucchi@unibas.ch, Assessor) |
| Content | This seminar will cover the foundations of learning theory. The main topics discussed in the seminar will be PAC learning, Rademacher Dimension Complexity and VC-dimension, Kernel methods, and Concentration inequalities. Each participant will give a presentation and write a report about a chosen topic. The assignment of the topics will be done the first week of the semester and presentations will start on week 3. |
| Learning objectives | * Reading and understanding scientific literature. * Preparing and presenting scientific talks. * Conducting scientific discussions with peers. * Writing and discussing scientific reports. |
| Bibliography | - Learning with Kernels Support Vector Machines, Regularization, Optimization, and Beyond B. Schölkopf, A. Smola - Foundations of Machine Learning M. Mohri, A. Rostamizadeh, and A. Talwalkar - High-dimensional statistics: A non-asymptotic viewpoint M. J. Wainwright - Understanding Machine Learning: From Theory to Algorithms S. Shalev-Shwartz, S. Ben-David |
| Admission requirements | The number of participants is limited to 20. Students in the M.Sc. or M.A. computer Science and mathematics may be given priority if the course is oversubscribed, but the course is also open to Bachelor students (then based on a first-come first-served basis). Prerequisites: * Probability theory * Linear algebra |
| Course application | registration via https://services.unibas.ch |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Thursday | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Date | Time | Room |
|---|---|---|
| Thursday 24.02.2022 | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 03.03.2022 | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 10.03.2022 | 14.15-16.00 | Fasnachtsferien |
| Thursday 17.03.2022 | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 24.03.2022 | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 31.03.2022 | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 07.04.2022 | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 14.04.2022 | 14.15-16.00 | Ostern |
| Thursday 21.04.2022 | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 28.04.2022 | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 05.05.2022 | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 12.05.2022 | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 19.05.2022 | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 26.05.2022 | 14.15-16.00 | Auffahrt |
| Thursday 02.06.2022 | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Modules |
Doctorate Computer Science: Recommendations (PhD subject: Computer Science) Electives Master Mathematics: Recommendations (Master's Studies: Mathematics) Modul: Concepts of Machine Intelligence (Master's degree subject: Computer Science) 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 | Seminar participants must - write a written report on their seminar topic (45%) - give a presentation on their topic (45%) - give written feedback (peer review) on another participant's report (10%) |
| 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 |