Course Catalog 2006-2007

SGN-6456 COMPUTATIONAL MODELS IN COMPLEX SYSTEMS, 4 cr
Computational Models in Complex Systems

Lecturers
Bela Patkai
Pauli Rämö

Lecturetimes and places
Per II,III: Monday 12 - 13, TB223
Per II,III: Monday 13 - 14, TB223

Implementations
  Period 1 Period 2 Period 3 Period 4 Period 5 Summer
Lecture - 1 h/week 1 h/week - - -
Exercise - 2 h/week 2 h/week - - -
Exam  
(Timetable for academic year 2006-2007)

Objectives
- Introduce a large amount of examples, models, and concepts in complex systems. - Introduce mathematical tools and methods that are used in complex systems. - Practice programming of models with a programming language (Matlab). - After the course the student: a) Knows what kind of scientific methods are available for complex systems b) Knows the basic properties of these methods c) Is able to code a simple method or a model d) Is able to analyze the results - Prepare the student for the other Complex Systems courses.

Content
Content Core content Complementary knowledge Specialist knowledge
1. Mathematical methods in Complex systems:
- Algorithmic complexity
- Fractals
- Non-linear dynamics
- Chaos theory
- Cellular automata
- Power laws
- Self-organized criticality
- Complex networks
- Evolution
- Genetic algorithms
- Pattern formation
- Synchronization phenomena
- Game theory
- Autonomous agents
- Artificial life
 		      
2. Programming models of complex systems
- Matlab
- Netlogo
 		      
3. Systemic view on solving complex problems       

Requirements for completing the course
Written examination and computer exercises (min. 50%)

Evaluation criteria for the course

  • Examination. Students may earn extra points for the exam with computer exercises.

  • Used assessment scale is numeric (1-5)

    • Study material
    Type Name Auhor ISBN URL Edition, availability... Exam material Language
    Lecture slides Computational Models for Complex Systems Pauli Rämö       Yes  English 

    Prerequisites
    Prequisite relations (Sign up to TUT Intranet required)

    Additional information about prerequisites
    Basic mathematics courses passed.

    Remarks

  • The course is suitable for postgraduate studies.

    • Distance learning

  • ITC utilized during the course

    • - In information distribution via homepage, newsgroups or mailing lists, e.g. current issues, timetables
    - In distributing and/or returning exercise work, material etc

  • Estimate as a percentage of the implementation of the course

    • - Contact teaching: 35 %
    - Distance learning: 0 %
    - Proportion of a student's independent study: 65 %

  • Description of the course implementation from ICT point of view

    • 50% of the laboratory exercises are mandatory. Lectures are not mandatory. Exam consists of 6 questions of which some are essays.

    Scaling
    Methods of instructionHours
    Lectures 36
    Exercises 72

    Other scaledHours
    Exam/midterm exam 3
    Total sum 111

    Principles and starting points related to the instruction and learning of the course

  • Lecture teaching is intensive and the teacher explains every topic with the blackboard and slides. Computer exercises allow more student-teacher interaction. Students prepare the given exercises with a computer and the teacher is always present to help if needed. Students may also present questions that are related to the lecture topics.

    • Additional information related to course
    Complexity arises in situations where the system parts (or agents) interact with each other in a complicated "emergent" fashion. Complex Systems is a highly multidisciplinary research topic that aims to understand complex behaviour and solve practical problems that arise in numerous different situations. This course introduces mathematical tools and concepts that are widely used in the complex systems community.

    Course homepage

    Last modified 27.08.2006
    Modified byPauli Rämö