Syllabus for |
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| TIF150 - Information theory for complex systems |
| Informationsteori för komplexa system |
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| Syllabus adopted 2019-02-14 by Head of Programme (or corresponding) |
| Owner: MPCAS |
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7,5 Credits
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| Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail |
| Education cycle: Second-cycle |
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Major subject: Bioengineering, Chemical Engineering, Engineering Physics |
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Department: 70 - SPACE, EARTH AND ENVIRONMENT
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Teaching language: English
Application code: 11111
Open for exchange students: Yes
Block schedule:
B+
| Module |
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0107 |
Examination |
7,5c |
Grading: TH |
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7,5c
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19 Mar 2021 pm J, |
Contact examiner, |
19 Aug 2021 am J |
In programs
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (elective)
Examiner:
Kristian Lindgren
Go to Course Homepage
Eligibility
General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
Basic undergraduate mathematics and probability theory.
Aim
The course introduces the students to important concepts in information theory that can be used to describe and characterise complex systems. The concepts are applied to a number of areas in complex systems: cellular automata, fractals, chemical self-organisation, and chaos. The main aim is to give students the knowledge and skills to apply information theory to a wide variety of different systems. The course also gives a presentation of the connections between information theory and physics, primarily statistical mechanics and thermodynamics.
Learning outcomes (after completion of the course the student should be able to)
After successfully completing this course the students will be able to
Define and use the basic concepts of information theory: Shannon entropy, relative entropy, complexity measures based on these
Use information theory to characterise both cellular automata and low-dimensional chaos
Understand the connection between information theory and statistical mechanics
Use geometric information theory to characterise patterns in spatically extended systems like pictures
Explain how information is flowing in chemical self-organising systems exhibiting pattern formation
Content
Basic concepts of information theory
- Shannon entropy, relative information, complexity measures
Information theory for symbol sequences and lattice systems
- correlations and randomness in symbol sequences
Information theory and physics
- entropy in physics and its relation to randomness in information theory
Cellular automata
- order and disorder in the time evolution of various cellular automaton rules
Geometric information theory
- presentation of an information-theoretic framework for characterising patterns
Self-organising chemical systems
- flows of information in the process of pattern formation
Chaotic systems and information
- flows of information from micro to macro in chaotic systems
Organisation
The course is based on a series of lectures, in total 30 hours, covering the topics listed above. Every week a set of home assignments are distributed. An optional mini project can also be done.
Literature
K. Lindgren, Information theory for complex systems - An information perspective on complexity in dynamical systems, physics, and chemistry. Lecture notes, Chalmers, 2014.
Examination including compulsory elements
The examination will be based on a final written exam, with possibility of extra points from homework assignments and the optional mini project.