APP MTH 7045 - Applied Mathematics Topic B

North Terrace Campus - Semester 1 - 2024

This course is available for students taking a Masters degree in Mathematical Sciences. The course will cover an advanced topic in applied mathematics. For details of the topic offered this year please refer to the Course Outline.

  • General Course Information
    Course Details
    Course Code APP MTH 7045
    Course Applied Mathematics Topic B
    Coordinating Unit Mathematical Sciences
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Available for Study Abroad and Exchange Y
    Assessment Ongoing assessment, exam
    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
    Complex network modelling and inference.


    Networks are all around us -- friend networks, communications networks, and transportation networks to name but three. There is a need to both understand these to improve our lives and relationships, and to better engineeer components to make our critical infrastructure more reliable and efificient.

    This course will study graphs and networks, and their generalisations as applied to modelling of complex systems of interacting components or actors with the view of achieving improve behaviour.

    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 in the Julia programming language, which is similar to Matlab. Solid experience with any of Python, Matlab, R or Julia should be sufficient background.

    Some knowledge of graph theory and/or Discrete Maths 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)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.


    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.


    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.


    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.


    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.


    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

  • Learning Resources
    Required Resources
    Most course materials are available online either through MyUni or
    Recommended Resources
    Resources will be provided through MyUni.

    Online Learning
    This course uses MyUni exclusively for providing electronic  resources, such as lecture notes, assignment papers, and sample  solutions.  Students should make appropriate use of these  resources.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course relies on lectures and exercises as the primary learning mechanism for the material. A sequence of written and/or online assignments provides assessment opportunities for students to gauge their progress and understanding.

    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.
    ActivityQuantityWorkload Hours
    Lecture classes   24   72
    Assignments/assessment   8   48
    Mini-Project 1 36
    Total   156
    Learning Activities Summary
    1. Mathematical Modelling
    2. Graph and Network Theory
    3. Statistical Inference of Networks
    4. Graph Algorithms
    5. Network Data Science
  • 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
    Component Weighting Objective Assessed
    Assignments 30% all
    Mini-project 30% all
    Competition 10% all
    Test 30% all
    Assessment Related Requirements
    An aggregate score of 50% is required to pass the course.
    Assessment Detail

    No information currently available.

    Homework assignments must either be given to the lecturer in person or submitted via MyUni by the given due time.  Failure to meet the deadline without reasonable and verifiable excuse may result in a significant penalty for that assignment.
    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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