COMP SCI 4408 - Modelling and Analysis of Complex Systems
North Terrace Campus - Semester 1 - 2018
General Course Information
Course Code COMP SCI 4408 Course Modelling and Analysis of Complex Systems Coordinating Unit School of Computer Science Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact 2 hours per week Available for Study Abroad and Exchange Prerequisites COMP SCI 2201 Course Description This course presents an overview of existing theories and methodologies for the analysis of complex systems behaviour. It focuses on the practical application of existing methods to the modelling and analysis of real-life complex systems, and on the identification and analysis of properties such as self-organisation, emergence and adaptability among others.
Course Coordinator: A/Prof Claudia Szabo
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn successful completion of this course students will be able to:
1. Develop knowledge of modeling, simulation and analysis techniques aimed at understanding complex systems.
2. Employ various modeling and analysis techniques to real-life complex systems problems.
3. Read and understand scientific research papers and present them in a seminar talk.
The above course learning outcomes are aligned with the Engineers Australia Stage 1 Competency Standard for the Professional Engineer.
The course is designed to develop the following Elements of Competency: 1.1 1.2 2.1 2.2 3.2 3.6
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2,3 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1,2,3 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
1,2 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1,2,3 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
2,3 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Recommended ResourcesThere are no textbooks for this course. However, a number of research papers and other resources will be made available on the course website.
Online LearningMore information about the course can be found online on myUni.
Learning & Teaching Activities
Learning & Teaching ModesThe course aims to introduce students to a wide range of complex systems modeling and analysis terminology, techniques, and processes. The concepts taught in these lectures will be practiced and reinforced by participation in three projects and the reading and reporting on research papers.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
This course is a 3 unit course. The expectation is that students will be spending 12 hours per week working on the course. Students are required to attend weekly lectures; the remainder of the time should be spent working on projects and the research papers. Students are expected to learn the content presented in lectures by doing the projects. They will gain additional knowledge by preparing a presentationthat is based on a research paper and reports that summarizes the research results of the research work they have to present.
Learning Activities SummaryThe following topics will be covered in the lectures:
* modeling and simulation techniques: analytical models, agent-based modeling among others
* complex systems properties: emergence, self-organisation, criticality, adaptability among others
* metrics and theories to identify complex systems behaviors/properties: micro/macro interactions, entropy among others
* analysis techniques: regression, ensemble modelling, statistical extrapolation, metrics, decision making, deep learning, fast frugal trees
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Proportion of final mark Abstraction Design Teamwork concepts & issues Interpersonal communications Data and information Programming Systems development Practical 1 10% 5 5 3 5 5 3 Practical 2 10% 5 5 3 5 5 3 Practical 3 10% 5 5 3 5 5 3 Research essay 30% 4 3 3 2 Seminar presentation 30% 4 3 3 2 Reflection exercises 2x2.5% 4 3 3 2 Presentation feedback 2x2.5% 4 3 3 2
Due Dates: The assignment due dates will be made available on the course website.
*CBOK categories are explained in section 4 of the ICT core body of knowledge. Numbers assigned correspond to the Bloom taxonomy (see page 26 of the same document).
Assessment DetailAssessment is by way of three programming assignments (practicals) and two presentations and reports of research papers. All assignments will be available on the course website.
No information currently available.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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