ITI42622 Complex Systems Modelling and Optimization (Spring 2024)
Facts about the course
- ECTS Credits:
- 10
- Responsible department:
- Faculty of Computer Science, Engineering and Economics
- Campus:
- Halden
- Course Leader:
- Stefano Nichele
- Teaching language:
- English
- Duration:
- ½ year
The course is connected to the following study programs
Elective course in Master in Applied Computer Science. Full-time and part-time.
Recommended requirements
-
Background in computer science or engineering
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Programming skills
Lecture Semester
Second semester (spring) in the full-time programme.
Fourth semester (spring) in the part-time programme.
The student's learning outcomes after completing the course
Knowledge
The student
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has a solid understanding of complex systems theory and modelling, including cellular automata, network, and agent-based models
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has knowledge on how to use optimization methods to program complex systems to produce a wanted behavior, in particular using biologically inspired methods
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has a clear understanding of key concepts in complex systems such as emergence, self-organization, adaptation, evolution.
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can relate underlying concepts and general principles from different complex systems
Skills
The student is able to
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model and analyse complex systems using cellular automata, networks and agent-based models
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program complex systems and optimize them using biologically inspired tools
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design and implement evolutionary methods
General competence
The student:
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has theoretical and practical understanding of complex systems modelling
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can understand and discuss relevance, strength and limitations of complex systems models and biologically inspired optimization methods
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is able to work in relevant research projects
Content
One of the challenges in our digitalized society is to model and predict the behavior of the complex systems that surround us. Complex systems are systems made of a large set of components interacting locally and giving rise to an emergent behavior without centralized control. Complex systems are all around us, for example social networks, the neurons in the brain, an artificial neural network, the stock market, the weather, a smart city, a biological ecosystem, virus spread, and many more.
This course will introduce different complex systems models, such as cellular automata, networks, and agent-based models, and how to program and use them to model real world system. Methods for programming complex systems models will include an introduction to biologically inspired optimization methods (such as artificial evolution).
Forms of teaching and learning
The course consists of lectures and seminars on techniques and methods, as well as a project to be carried out individually or in groups of 2-3 students. The project will be chosen from a portfolio of available problems. The students must submit both code and a project report.
Workload
Approx. 280 hours.
Examination
Project (individual or in groups of 2-3 students) and individual oral exam
The exam is divided into two parts:
-
50% of the grade based on the project. All students in the group will share the same grade.
-
The individual oral exam (50%) is based on the course curriculum. Duration approx. 20-30 min. No supporting materials are allowed.
Grading scale A - F in both parts. Both parts of the exam must be passed to pass the course. The student will get an individual joint grade for the entire course.
Examiners
External and internal examiner, or two internal examiners.
Conditions for resit/rescheduled exams
Upon re-examination, each part of the examination can be retaken. Upon re-examination, a new project must be carried out.
Course evaluation
This course is evaluated by a:
-
Mid-term evaluation (compulsory)
The responsible for the course compiles a report based on the feedback from the students and his/her own experience with the course. The report is discussed by the study quality committee at the Department of Computer Science and Communication.