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Introduction to Economic Modelling with Computational Intelligence

NOVA Master's course of 5 ECTS, organised by University Lecturer Stefan Bäckman, University of Helsinki - Faculty of Agriculture and Forestry.
Dates and location: 5-9 September 2016 in Helsinki, Finland. NB! The course can also be taken as a distance learning course.

Introduction to Economic Modelling with Computational Intelligence
Shutterstock, Mila Supinskaya
Course Description
The course aims at providing basic knowledge and skills for constructing computer models with computational intelligence methods. Skills for economical applications in this area are also provided. To some extent, knowledge and skills of statistical modelling are empowered. A cutting-edge, multi-disciplinary approach to model construction will be adopted.

Computational intelligence (CI) generally includes such methods as neural networks, fuzzy systems, evolutionary computing, Bayesian networks, cellular automata and swarm theory. Today CI is applied to systems and models in control, decision making, pattern recognition, robotics, data mining, biological and social modelling and statistics, inter alia. In particular, they are useful when non-parametric or non-linear models are constructed, and they often yield better and simpler computer models than the corresponding classical mathematical models.

Many models and theoretical results of CI are already available in economics, but their application is still uncommon and fairly unfamiliar in the Nordic countries (possibly excluding Finland). Hence, there is a justified need for a course which deals with the basics and economical applications of CI.

Please find more information on the course on the University of Helsinki's course webpage.

The course focuses on fuzzy systems, neural networks and evolutionary computing. It presents the basic principles and economical applications of these methods in computer modelling. Corresponding traditional mathematical and statistical models are also considered. During the course days fuzzy rule-based economical computer models are designed and constructed, and they are fine-tuned with neural networks and genetic algorithms. Fuzzy cognitive maps, both numerical and linguistic, are also examined because these are simple and usable models for examining complex phenomena. CI models are compared to such corresponding traditional methods as regression, cluster, discriminant and time series analysis.

Programme Outline
The course days include 4-5 hours of lectures, which can also be followed live with Adobe Acrobat connection. Lectures comprise of theory parts on computational intelligence and exercises on economical model constructions with Matlab.

Students can participate and complete the course from their home countries if they wish to. In that case they need to follow the lectures online with Adobe connection. The teacher has good experiences of this kind of teaching. Students are able to ask questions during the lectures via the programme.

Learning Outcomes
The students shall be given possibilities to work with fuzzy modelling in economics. The students will see an introduction to newly developed methods in neural networks and genetic algorithms.


  • highly specialised, novel, cutting-edge and multi-disciplinary knowledge on computational intelligence modelling, especially in economic applications.


  • specialised problem-solving skills for constructing computer models with computational intelligence.


  • ability to manage and transform computational intelligence modelling to work or study contexts that are complex, unpredictable and require novel strategic approaches.

Evaluation Elements
A course report should be completed and sent for evaluation within a month after the on-site course days. The report (5-7 pages) should present a simple economical application of fuzzy, neuro-fuzzy or genetic-fuzzy model. These application ideas will be considered and discussed with the participants during the course.

Pedagogical Approach
Moodle learning environment is used for learning materials, discussions and for delivering and evaluating the course reports.

Estimated Workload

Prerequisite Knowledge
Matlab software is used for the assignments, but previous knowledge of the software is not required. Basic (high school level) knowledge of mathematics is recommended.

Admission for NOVA courses is handled by the course organiser/ the NOVA member institution organising the course. Please see the links in the margin for more information.

Apply here:

  1. Fill in this Word form and send it to Mr. Niskanen by email (rtf file)
  2. Fill also in this short e-form
Published 30. May 2016 - 17:14 - Updated 3. April 2018 - 15:12