Course title: 
Data-driven and agent-based modeling
Primary programme: 
Fizikus mérnök BSc
ECTS credits: 
Course type: 
Number of lectures per week: 
Number of practices per week: 
Number of laboratory exercises per week: 
Further knowledge transfer methods: 
laboratory exercise, project work
Coursework grade
Special grading methods: 
class activity, home works, project work
Introduction to Machine Learning, Introduction to Experimental Data Handling
Responsible lecturer: 
Dr. Titusz Fehér, associate professor, PhD
Lecturers and instructors: 
Course description: 
This course gives an introduction to data-based and agent-based modelling, based on examples of application. Most of these examples are based the instructors’ own previous research experience. Data-based modelling: Principles, aim, accuracy, assessment of reliability. Data exploration, filtering, transformation. Modeling by advanced maximum likelihood methods, optimization techniques. Agent-based modeling: Principles, aim, validation. Examples include, minority games, biological (flocking, ant colony, etc.), economical (minority games, stock market, etc.), social (opinion, segregation, etc.) and game theory problems.
Reading materials: 
Numerical Recipes 3rd Edition: The Art of Scientific Computing (Cambridge University Press, 2007, ISBN 978-0521880688) Wilensky, Uri, and William Rand. An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. (MIT Press, 2015, ISBN-13: 978-0262731898) Railsback, Steven F., and Volker Grimm. Agent-based and individual-based modeling: a practical introduction. (Princeton university press, 2019. ISBN-13: 978-0691136745)
List of competences: 
Please find the detailed list, as quoted from the Hungarian training and outcome requirements of the Physicist Engineer program, in the Hungarian version of the course description.