Admission requirements
Not applicable.
Description
Natural computing is a quickly developing field dealing with models and computational paradigms inspired by nature and attempts to understand the world around us in terms of information processing. Natural computing today includes paradigms such as modelling information processing through artificial neural networks, modelling emergent behaviour resulting from the interaction of a large collection of agents in particle swarms (representing e.g., birds, insects) or spatial arrangements of cells (cellular automata), or modelling efficient search and optimization procedures such as ant colonies (finding shortest paths in a network of possibilities), simulated annealing processes (finding the optimal energy state of a crystal), and evolutionary processes (adapting a population to find the best mix of genetic material under changing environmental conditions). The course introduces the foundations of a variety of such computational paradigms, and discusses algorithmic implementations on computers as well as the analogies between these implementations and the natural model. In addition, we also present some practical application examples of such computational paradigms, such as pattern recognition, engineering optimization, simulations of fire breakouts, to name a few.
Course objectives
The course gives a comprehensive overview of the field through a series of lectures and exercises. In addition, a practical application exercise of natural computing algorithms is given to the students, who are expected to run experiments and write a short report about the experiment and the results obtained. By attending the course, students
learn about the main classes of natural computing algorithms,
acquire practical skills in implementing and applying instances of natural computing algorithms, and
learn how to write a short report in the format of a scientific paper.
Timetable
Het meest recente rooster is te vinden op de Studenten-website:
Mode of instruction
Weekly lectures
Assignment
Assessment method
- the written exam (70%)
- the report about the practical assignment (30%)
In order to pass the course, grades for both of these items should be at least 5.5.
Reading list
Slides contain all necessary material covered by this course. The following book is recommended but not mandatory for the course: Leandro Nunes de Castro, Fundamentals of Natural Computing, Chapman & Hall/CRC.
Registration
Aanmelding voor vakken verloopt via uSis. Hiervoor is de uSis-code van het vak nodig, die te vinden zijn in de Studiegids. Meer info over het inschrijven voor vakken of tentamens is hier te vinden.
MyTimetable
In MyTimetable kun je alle vak- en opleidingsroosters vinden, waarmee jij je persoonlijke rooster kunt samenstellen. Onderwijsactiviteiten waarvoor je in uSis staat ingeschreven, worden automatisch in je rooster getoond. Daarnaast kun je My Timetable gemakkelijk koppelen aan een agenda-app op je telefoon en worden roosterwijzigingen automatisch in je agenda doorgevoerd; bovendien ontvang je desgewenst per e-mail een notificatie van de wijziging.
Vragen? Bekijk de video-instructie, lees de instructie of neem contact op met de ISSC helpdesk.
Brightspace
Inschrijving voor vakken verloopt via uSis. Wanneer je je hier inschrijft voor een bepaald vak krijg je automatisch ook toegang tot de omgeving van dit vak via Brightspace.
Voor meer informatie over Brightspace kun je op deze link klikken om de handleidingen van de universiteit te bekijken. Bij overige vragen of problemen kan contact opgenomen worden met de helpdesk van de universiteit Leiden.
Contact
Onderwijscoördinator Informatica, Riet Derogee
Dr Anna V. Kononova a.kononova@liacs.leidenuniv.nl
Diederick Vermetten d.l.vermetten@liacs.leidenuniv.nl