APP MTH 7088 - Applied Mathematics Topic F

North Terrace Campus - Semester 2 - 2018

Please contact the School of Mathematical Sciences for further details, or view course information on the School of Mathematical Sciences web site at

  • General Course Information
    Course Details
    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
    Course Staff

    Course Coordinator: Professor Matthew Roughan

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    In 2017 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".

    Learning Outcomes:

    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)   
    - Simulation   
    - Algorithms

    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)
    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
  • Learning Resources
    Required Resources
    All required materials will be provided. 
    Recommended Resources
    "Networks: An Introduction", M.E.J. Newman, Oxford Uni Press, 2010.
    Online Learning
    MyUni 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 Modes
    The 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 Summary
    Lecture Outline
    1. Basics
      • graph theory basics
      • graph metrics
      • random graph models
      • algorithms on graphs
    2. Advanced Topics
      • graph generalisations: hyper-graphs and meta-graphs
      • graph algebras
    3. Inference
      • sampling from networks
      • inference on graphs: network tomography
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    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 Requirements
    An aggregate score of 50% is required to pass the course.
    Assessment Detail

    No information currently available.

    All 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.
    Course Grading

    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.

  • Student Feedback

    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 ( 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.

  • Student Support
  • Policies & Guidelines
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