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| Semester | spring semester 2023 |
| Course frequency | Irregular |
| Lecturers | Aurelien Lucchi (aurelien.lucchi@unibas.ch, Assessor) |
| Content | This course provides an in-depth theoretical treatment of classical and modern optimization methods. Specifically, we will discuss the following concepts: Basic convex analysis Subgradient methods Gradient Descent Convergence rate for gradient-based methods Optimality and lower bounds Stochastic Optimization methods Non-convex Optimization |
| Learning objectives | - Equip students with a fundamental understanding of why optimization algorithms work, and what their limits are - Ability to select optimization algorithms for practical applications that students might encounter in their future career - Ability to understand and derive mathematical proofs for optimization algorithms |
| Bibliography | The first part of the class will cover some of the chapters discussed in the following books: Numerical Optimization, by Jorge Nocedal and Stephen J. Wright Convex Optimization: Algorithms and Complexity, by Sebastian Bubeck Convex Optimization, by Stephen Boyd and Lieven Vandenberghe The second part of the class will mostly be based on research papers. |
| Comments | Exceptionally, the first lecture will be given on Monday, February 20 from 4.15pm to 6pm in Alte Uni, Seminarraum -201. There won't be any lecture on Thursday, February 23rd. Starting week 2: Exercise sessions will start the second week of the semester and will be scheduled every Monday (4.15pm to 6pm). The lectures will be scheduled on Thursdays from 4.15pm to 6pm. |
| Weblink | https://dmi.unibas.ch/de/studium/compute |
| Admission requirements | A solid background in analysis and linear algebra; some background in theoretical computer science (computational complexity, analysis of algorithms); the ability to understand and write mathematical proofs. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Thursday | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Date | Time | Room |
|---|---|---|
| Monday 20.02.2023 | 16.15-18.00 | Alte Universität, Seminarraum -201 |
| Thursday 23.02.2023 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 02.03.2023 | 16.15-18.00 | Fasnachstferien |
| Monday 06.03.2023 | 16.15-18.00 | Kollegienhaus, Seminarraum 103 |
| Thursday 09.03.2023 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 16.03.2023 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 23.03.2023 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 30.03.2023 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 06.04.2023 | 16.15-18.00 | Ostern |
| Thursday 13.04.2023 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 20.04.2023 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 27.04.2023 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 04.05.2023 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 11.05.2023 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 18.05.2023 | 16.15-18.00 | Auffahrt |
| Thursday 25.05.2023 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Thursday 01.06.2023 | 16.15-18.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Modules |
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: Methods of Machine Intelligence (Master's Studies: Computer Science) |
| Assessment format | continuous assessment |
| Assessment details | Continuous assessment Note the following split: 30% homework 30% project (writeup and presentation) 40% written exam A 50% score on HW sets is required to participate in the final exam. Expected date: Monday, 10 July 2023, 10-12 a.m., Spiegelgasse 1, room 00.003. |
| 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 |