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67923-01 - Lecture: Continuous Optimization 6 CP

Semester spring semester 2024
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 Exercise sessions will start the second week of the semester and will be scheduled every Monday (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

Dates

Date Time Room
Thursday 29.02.2024 16.15-18.00 Spiegelgasse 1, Seminarraum 00.003
Thursday 07.03.2024 16.15-18.00 Spiegelgasse 1, Seminarraum 00.003
Thursday 14.03.2024 16.15-18.00 Spiegelgasse 1, Seminarraum 00.003
Thursday 21.03.2024 16.15-18.00 Spiegelgasse 1, Seminarraum 00.003
Thursday 28.03.2024 16.15-18.00 Ostern
Thursday 04.04.2024 16.15-18.00 Spiegelgasse 1, Seminarraum 00.003
Thursday 11.04.2024 16.15-18.00 Spiegelgasse 1, Seminarraum 00.003
Thursday 18.04.2024 16.15-18.00 Spiegelgasse 1, Seminarraum 00.003
Thursday 25.04.2024 16.15-18.00 Spiegelgasse 1, Seminarraum 00.003
Thursday 02.05.2024 16.15-18.00 Spiegelgasse 1, Seminarraum 00.003
Thursday 09.05.2024 16.15-18.00 Auffahrt
Thursday 16.05.2024 16.15-18.00 Spiegelgasse 1, Seminarraum 00.003
Thursday 23.05.2024 16.15-18.00 Spiegelgasse 1, Seminarraum 00.003
Thursday 30.05.2024 16.15-18.00 Spiegelgasse 1, Seminarraum 00.003
Modules Doctorate Computer Science: Recommendations (PhD subject: Computer Science)
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: Concepts of Machine Intelligence (Master's Studies: Computer Science)
Module: Methods of Machine Intelligence (Master's Studies: Computer Science)
Module: Systems Foundations (Master's Studies: Data 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, 8 July 2024, 10-12 a.m..
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

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