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32934-01 - Lecture: Optimization and AI 3 CP

Semester spring semester 2022
Course frequency Every spring sem.
Lecturers Dietmar Maringer (dietmar.maringer@unibas.ch, Assessor)
Content 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.
Learning objectives 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.
Bibliography 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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Admission requirements *) 58989 Computing for Business and Economics (or equivalent)
*) basic Python programming skills

Course application 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!
Language of instruction English
Use of digital media Online course

 

Interval Weekday Time Room
wöchentlich Thursday 14.15-18.00 - Online Präsenz -

Dates

Date Time Room
Thursday 24.02.2022 14.15-18.00 - Online Präsenz -, --
Thursday 03.03.2022 14.15-18.00 - Online Präsenz -, --
Thursday 10.03.2022 14.15-18.00 Fasnachtsferien
Thursday 17.03.2022 14.15-18.00 - Online Präsenz -, --
Thursday 24.03.2022 14.15-18.00 - Online Präsenz -, --
Thursday 31.03.2022 14.15-18.00 - Online Präsenz -, --
Thursday 07.04.2022 14.15-18.00 - Online Präsenz -, --
Modules Module: Field Electives in Economics and Public Policy (Master's Studies: Economics and Public Policy)
Module: Risk Analysis (Master's Studies: Actuarial Science)
Module: Specific Electives in Data Science and Computational Economics (Master's Studies: Business and Economics)
Module: Technology Field (Master's Studies: Business and Technology)
Specialization Module: Areas of Specialization in International and/or Monetary Economics (Master's Studies: International and Monetary Economics)
Specialization Module: Quantitative Methods (Master's Studies: Business and Economics (Start of studies before 01.08.2021))
Assessment format record of achievement
Assessment details Combination of active participation, assignment(s), and final exam.
written exam: 21.04.22; 14:15-15:00. WWZ S13: A-Z.
Assessment registration/deregistration Reg.: course registration, dereg: cancel course registration
Repeat examination no repeat examination
Scale 1-6 0,1
Repeated registration as often as necessary
Responsible faculty Faculty of Business and Economics , studiendekanat-wwz@unibas.ch
Offered by Faculty of Business and Economics

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