APP MTH 7048 - Applied Mathematics Topic A

North Terrace Campus - Semester 1 - 2021

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 7048
    Course Applied Mathematics Topic A
    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: Dr Andrew Black

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    In 2021 the topic of this course will be Advanced stochastic modelling and Monte Carlo methods

    Stochastic models are a broad class of mathematical models that are used to describe phenomena that are characterised by uncertainty or randomness. There are numerous examples of such phenomena: biological populations, epidemics, financial markets, traffic systems—one can find examples practically everywhere. They may be ubiquitous, but almost no useful models can be solved analytically, so instead we develop Monte Carlo approaches that involve the generation of random processes via a computer. These techniques are not just useful for simulation of stochastic models, but also a powerful method for solving many deterministic type problems. This course will offer a deep dive into the use of randonmess to solve models and problems that would otherwise be intractable. 

    Assumed knowledge
    Applied Probability III or Random Processes III is the best preparation, but a thorough knowledge of Probability and Statistics II is enough.
    Knowledge of basic Baysian inference and Markov chain Monte carlo is useful, but not required. 
    This course will require extensive programming (students can choose their language, but the use of Julia is encoraged).

    On successful completion of this course, students will be able to:
    1. Understand and apply various simple and advanced Monte Carlo techniques.
    2. Simulate and use models based on stochastic differential equations.
    3. Construct and simulate continuous time Markov chain models.
    4. Understand and apply stochastic models and methods for inference of hidden dynamical processes.
    5. Present analysis in written and graphical 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
    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
    Access to the intranet.
    Recommended Resources
    D. P. Kroese, T. Taimre, Z. I. Botev, Handbook of Monte Carlo Methods, Wiley 2011
    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
    Students will work though the notes and reading materials guided by the lecturer. Weekly workshops will provide time for in depth discussion of the material. 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.

    ActivityQuantityWorkload Hours
    Study 30 108
    Assignments 4 48
    Total 156

    Learning Activities Summary
    1. Monte Carlo methods (weeks 1-4)
    2. Stochastic differential equation models with financial applications (weeks 5-6)
    3. Continuous-time Markov chain models (week 6)
    4. Markov chain Monte Carlo (week 7)
    5. Learning and inference for dynamical systems (weeks 8-12)
    Specific Course Requirements
  • 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 60% all
    Mini-projects 30% all
    Participation 10% all
    Assessment Related Requirements
    An aggregate score of at least 50% is required to pass the course.
    Assessment Detail
    There will be 4 assignments and 2 mini-projects, spaced equally over the semester.
    All assignments are to be submitted online through MyUni. Failure to meet the deadline without reasonable and verifiable excuse may result in a significant penalty.
    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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