APP MTH 7044 - Applied Mathematics Topic C

North Terrace Campus - Semester 1 - 2017

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 7044
    Course Applied Mathematics Topic C
    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 30%, Exam 70%
    Course Staff

    Course Coordinator: Dr Andrew Smith

    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 Stochastic Processes for Population and Epidemic Modelling.


    Randomness is an important factor in modelling and analyzing various real-life situations. In particular, stochastic (random) models can be used to make better informed decisions about changes in populations and epidemics. This course covers some key theory in the modelling of (meta)populations and epidemics, such as ODEs, discrete and continuous time Markov chains (DTMC and CTMC) and functional laws of large numbers.

    Learning Outcomes

    On successful completion of this course, students will be able to
    1.    explain the basic model structures used in population and epidemic 
    2.    develop both ODE and CTMC models of population, as well as infectious disease, dynamics
    3.    analytically derive stationary distributions for CTMCs of particular forms
    4.    numerically evaluate the distribution of the state of the CTMC models given initial conditions
    5.    gain a better appreciation for, and derive, the deterministic approximations to CTMCs 
    6.    a general appreciation of path integral methods for CTMCs
    7.    present analyses and intepretations in written form

    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
    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
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    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
  • Learning Resources
    Required Resources
    Recommended Resources
    1. Grimmett and Stirzaker, Probability and random processes, OUP, 2001
    2. Kreyszig, Advanced engineering mathematics, Wiley
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course relies on lectures as the primary delivery mechanism for the material. Three written assignments will help students to gauge their progress and understanding of the course.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity    Quantity    Workload Hours
    Lectures   30    90
    Assignments     3    68
    Total     158
    Learning Activities Summary
    Lecture Outline

    L1 - L4:    Deterministic models
    L5 - L11:    Stochastic models
    L12 - L18:    Deterministic limits
    L19 - L25:    Path integrals
    L26 - L28:    Bayesian inference
    L29:    Revision
  • 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
    Exam    70%    all
    Assignments    30%    all
    Assessment Detail
    There will be three assignments worth 30% of the total mark. The remaining 70% will come from the exam.
    Assignments must be handed in person to the lecturer or submitted in the assigned assignment box if they are to be marked.
    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
  • Fraud Awareness

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