Outline

Overview

The dynamics of complex systems are often described in terms of how they process information and self-organise; for example regarding how genes store and utilise information, how information is transferred between neurons in undertaking cognitive tasks, and how swarms process information in order to collectively change direction in response to predators. The language of information also underpins many of the central concepts of complex adaptive systems, including order and randomness, self-organisation and emergence. Shannon information theory, which was originally founded to solve problems of data compression and communication, has found contemporary application in how to formalise such notions of information in the world around us and how these notions can be used to understand and guide the dynamics of complex systems. This unit of study introduces information theory in this context of analysis of complex systems, foregrounding empirical analysis using modern software toolkits, and applications in time-series analysis, nonlinear dynamical systems and data science. Students will be introduced to the fundamental measures of entropy and mutual information, as well as dynamical measures for time series analysis and information flow such as transfer entropy, building to higher-level applications such as feature selection in machine learning and network inference. They will gain experience in empirical analysis of complex systems using comprehensive software toolkits, and learn to construct their own analyses to dissect and design the dynamics of self-organisation in applications such as neural imaging analysis, natural and robotic swarm behaviour, characterisation of risk factors for and diagnosis of diseases, and financial market dynamics.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Competency in 1st year mathematics, and basic computer programming skills are assumed. Competency in 1st year undergraduate level statistics (for example, covering probabilities, conditional probabilities, Gaussian distribution, correlations, statistical significance/hypothesis testing and p-values). An exposure to linear algebra would be useful but not mandatory
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Joseph Lizier, joseph.lizier@sydney.edu.au
Lecturer(s)	Joseph Lizier, joseph.lizier@sydney.edu.au
Tutor(s)	Jaime Ruiz Serra, jaime.ruizserra@sydney.edu.au

Assessment

The census date for this unit availability is 2 September 2024

Details
Criteria

Type	Description	Weight	Due	Length
Assignment	Project report Report on major project	35%	Formal exam period Due date: 11 Nov 2024 at 23:59	2000 words
Outcomes assessed:
Assignment	Information theory exercises Calculations and short answer questions	20%	Week 05 Due date: 01 Sep 2024 at 23:59	n/a
Outcomes assessed:
Small continuous assessment	Article review Summary and critical analysis of an article	15%	Week 07 Due date: 15 Sep 2024 at 23:59	500 words
Outcomes assessed:
Presentation	Project presentation Video presentation on major project	30%	Week 12 Due date: 27 Oct 2024 at 23:59	10 mins plus Q&A
Outcomes assessed:
= hurdle task ?

Assessment summary

Information theory exercises: You will demonstrate your understanding of the information-theoretic concepts taught in class in calculation exercises (involving both involve mathematical and computational tasks) and short answer questions.

Article Review: you will write a short appraisal of an article / report / blog post which provides an information-theoretic analysis of empirical data, and critically evaluate that study. The reviews will be socially shared and discussed in class.

Info theory project presentation and report: You will create an information-theoretic analysis of a data set of your choosing, selecting appropriate tools to answer a question of interest regarding relationships in that data. You will deliver a video presentation, and write a report, describing your approach, the results, discussing implications for the system under study, and critically evaluating your findings.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

All assessments must be repeated or replaced with different assessment if missed due to special consideration. No late submissions will be accepted without formal Special Consideration for the Literature Review and Project Presentation, since students would gain an unfair advantage by submitting late after seeing the work of other students.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Uncertainty and entropy 1	Lecture and tutorial (3 hr)
Week 02	Uncertainty and entropy 2	Lecture and tutorial (3 hr)
Week 03	What is information? 1	Lecture and tutorial (3 hr)
Week 04	What is information? 2	Lecture and tutorial (3 hr)
Week 05	JIDT software	Lecture and tutorial (3 hr)
Week 06	Information-theoretic estimators	Lecture and tutorial (3 hr)
Week 07	Statistical significance	Lecture and tutorial (3 hr)
Week 08	Self-organisation and case studies	Lecture and tutorial (3 hr)
Week 09	Information processing in complex systems	Lecture and tutorial (3 hr)
Week 10	Information storage	Lecture and tutorial (3 hr)
Week 11	Information transfer	Lecture and tutorial (3 hr)
Week 12	Network inference	Lecture and tutorial (3 hr)
Week 13	Project sharing and wrap up	Lecture and tutorial (3 hr)

Attendance and class requirements

Independant study: Students are expected to use independent study time to further develop their computing skills and to practise solving analytical problems.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas. Primary references include:

Bossomaier, Barnett, Harré and Lizier, An Introduction to Transfer Entropy: Information Flow in Complex Systems. Cham, Springer, 2016.
Cover and Thomas, Elements of Information Theory (2nd). New Jersey, John Wiley and Sons, 2006.
MacKay, David J.C., , Information Theory, Inference, and Learning Algorithms. Cambridge, Cambridge University Press, 2003.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. critically evaluate investigations of self-organisation and relationships in complex systems using information theory, and the insights provided
LO2. develop scientific programming skills which can be applied in complex system analysis and design
LO3. apply and make informed decisions in selecting and using information-theoretic measures, and software tools to analyse complex systems
LO4. create information-theoretic analyses of real-world data sets, in particular in a student’s domain area of expertise
LO5. understand basic information-theoretic measures, and advanced measures for time-series, and how to use these to analyse and dissect the nature, structure, function, and evolution of complex systems
LO6. understand the design of, and to extend the design of a piece of software using techniques from class, and your own readings.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

We have been running the class in flipped mode, and continue to adjust the activities here. We're also adding more support to both Python and Matlab notebooks to support students working on their own

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

CSYS5030: Information Theory and Self-Organisation

Semester 2, 2024 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect