APP MTH 7088 - Applied Mathematics Topic F
North Terrace Campus - Semester 2 - 2019
General Course Information
Course Code APP MTH 7088 Course Applied Mathematics Topic F Coordinating Unit School of Mathematical Sciences Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Y Course Description Please contact the School of Mathematical Sciences for further details, or view course information on the School of Mathematical Sciences web site at http://www.maths.adelaide.edu.au
Course Coordinator: Professor Matthew Roughan
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesIn 2019 the topic of this course will be Complex network modelling and inference.
This course will study graphs and networks, and their generalisations
as applied to modelling of complex systems of interacting components or
actors. We'll go somewhat beyond standard graph theory in that we will
consider how quantities associated with links affect real network
problems. In particular, we will consider how to statistically infer
properties of networks from realistically obtainable metrics when the
networks are large, and we cannot query the network directly, but must
use indirect measurement strategies. Applications range from management
of computer networks to analysis of social phenomenon, such as memes
that "go viral".
1. Modelling: model a problem
- Take a problem stated in words, and convert it into mathematical form
- Consider assumptions and approximations
- Deal with incomplete information/ideas by asking questions, and investigation
2. Analysis: analyse the problem using diverse tools
- Analysis (mathematical solution of problems)
- Statistics (incorporating data)
3. Critically examine results:
- Sanity checking
- Close the loop between modelling->analysis->output
- Sensitivity analysis
4. Communicate results
- Mathematical exposition skills
Pre-requisites and assumed knowledge:
Mathematics up to second year level will be required, including
- Probability and Statistics II,
- Scientific Computing or equivalent.
In particular, this project will require some programming (Matlab or another language is acceptable).
Some knowledge of graph theory would be useful.
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)
all 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
Required ResourcesAll required materials will be provided.
Recommended Resources"Networks: An Introduction", M.E.J. Newman, Oxford Uni Press, 2010.
Online LearningMyUni will be used to provide electronic resources, such as lecture notes, assignment papers, sample solutions, discussion boards, etc. It is recommended that the students make appropriate use of these resources.
Learning & Teaching Activities
Learning & Teaching ModesThe lecturer guides the students through the course material in 30 lectures. Students are expected to engage with the material in the lectures. Interaction with the lecturer and discussion of any difficulties that arise during the lecture is encouraged. Fortnightly homework assignments help students strengthen their understanding of the theory and their skills in applying it, and allow them to gauge their progress.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Lectures 24 60 Practicals/Tute 12 30 Assignments 6 66 Total 156
Learning Activities SummaryLecture Outline
- graph theory basics
- graph metrics
- random graph models
- algorithms on graphs
- Advanced Topics
- graph generalisations: hyper-graphs and meta-graphs
- graph algebras
- sampling from networks
- inference on graphs: network tomography
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 task Task type When due Weighting Learning outcomes Examination Summative Examination period 50% All Assignments Formative and summative Weeks 3, 5, 7, 9 and 11 50% All
Assessment Related RequirementsAn aggregate score of 50% is required to pass the course.
No information currently available.
SubmissionAll written assignments are to be submitted to the lecturer with a signed cover sheet attached. There will be a maximum two week turn-around time on assignments for feedback to students.
Late assignments will not be accepted, but students may be excused from an assignment for medical or compassionate reasons. In such cases, documentation is required and the lecturer must be notified as soon as possible before the fact.
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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