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32934-01 - Vorlesung: Optimization and AI 3 KP

Semester Frühjahrsemester 2022
Angebotsmuster Jedes Frühjahrsem.
Dozierende Dietmar Maringer (dietmar.maringer@unibas.ch, BeurteilerIn)
Inhalt Many fields in business and economics involve optimization problems. This course covers traditional (numerical) optimization methods, based on deterministic algorithms, including gradient-based and gradient-free methods, or concepts from constraint-satisfaction. While these mehods are highly efficient in some circumstances, they also have their limits: They are no longer reliable when objective functions have more than one optimum, special constraints or requirements need to be satisfied, or the nature oft he problem is simply too complex. For these cases, meta-heuristics and artificial intelligence (AI) inspired concepts can be used. We will look into simple stochastic methods like Monte Carlo Search and Simulated Annealing/Threshold Accepting, but also into population based methods that mimic evolutionary processes or swarm intelligence.

To deepen the participants' understanding of these methods, their practical application, their strengths and limitations, the course is very much hands-on, allowing for numerous own implementations and experiments.
Lernziele Successful participants should be familiar with numerical and computational methods for simple and demanding optimization problems. Also, they will improve their programming skills with special emphasis on the implementation of economic / management models and related methods.
Literatur Lecture material will be provided. There is no designated textbook, but to get a flavor of the topics or to deepen their knowledge, (prospective) participants might find the following books (in alphabetical order) helpful:

*) Gilli, M.; Maringer, D. & Schumann, E. Numerical Methods and Optimization in Finance, Academic Press, 2nd edition 2019. (or 1st ed., 2011)

*) Michalewicz, Z. & Fogel, D. B. How to Solve It: Modern Heuristics, Springer, 2005

*) Brabazon, A.; O'Neill, M. & McGarraghy, S. Natural Computing Algorithms, Springer, 2015

*) Hillier, F. & Liebermann, G., Introduction to Operations Research, McGraw-Hill, 11th ed., 2019.

*) Miranda, M. J. & Fackler, P. L. Applied Computational Economics and Finance The MIT Press, 2002

*) Brandimarte, P. Numerical Methods in Finance and Economics, Wiley-Interscience, 2006

Specific recommendations and additional literature to be announced during the course. how these can be setup, implemented, and analysed.
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Teilnahmebedingungen *) 58989 Computing for Business and Economics (or equivalent)
*) basic Python programming skills

Anmeldung zur Lehrveranstaltung Registration: Please enrol in MOnA. EUCOR-Students and students of other Swiss Universities have to enrol at the students administration office (studseksupport1@unibas.ch) within the official enrolment period. Enrolment = Registration for the exam!
Unterrichtssprache Englisch
Einsatz digitaler Medien Online-Veranstaltung


Intervall wöchentlich
Datum 24.02.2022 – 07.04.2022
Zeit Donnerstag, 14.15-18.00 - Online Präsenz -
Datum Zeit Raum
Donnerstag 24.02.2022 14.15-18.00 Uhr - Online Präsenz -, --
Donnerstag 03.03.2022 14.15-18.00 Uhr - Online Präsenz -, --
Donnerstag 10.03.2022 14.15-18.00 Uhr Fasnachtsferien
Donnerstag 17.03.2022 14.15-18.00 Uhr - Online Präsenz -, --
Donnerstag 24.03.2022 14.15-18.00 Uhr - Online Präsenz -, --
Donnerstag 31.03.2022 14.15-18.00 Uhr - Online Präsenz -, --
Donnerstag 07.04.2022 14.15-18.00 Uhr - Online Präsenz -, --
Module Modul: Field Electives in Economics and Public Policy (Masterstudium: Economics and Public Policy)
Modul: Risiko-Analyse (Masterstudium: Actuarial Science)
Modul: Specific Electives in Data Science and Computational Economics (Masterstudium: Wirtschaftswissenschaften)
Modul: Technology Field (Masterstudium: Business and Technology)
Spezialisierungsmodul: Areas of Specialization in International and/or Monetary Economics (Masterstudium: International and Monetary Economics)
Vertiefungsmodul: Quantitative Methods (Masterstudium: Wirtschaftswissenschaften (Studienbeginn vor 01.08.2021))
Leistungsüberprüfung Leistungsnachweis
Hinweise zur Leistungsüberprüfung Combination of active participation, assignment(s), and final exam.
written exam:
An-/Abmeldung zur Leistungsüberprüfung An-/Abmelden: Belegen resp. Stornieren der Belegung via MOnA
Wiederholungsprüfung keine Wiederholungsprüfung
Skala 1-6 0,1
Wiederholtes Belegen beliebig wiederholbar
Zuständige Fakultät Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch
Anbietende Organisationseinheit Wirtschaftswissenschaftliche Fakultät / WWZ