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63987-01 - Seminar: Statistical Learning Theory and Related Topics 4 CP

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 weekly
Date 24.02.2022 – 02.06.2022
Time 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./dereg.: course registr./cancel registr. via MOnA
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

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