MATHS 2203 - Advanced Mathematical Perspectives II

North Terrace Campus - Semester 2 - 2022

The aim of this course is to foster a broad appreciation of the mathematical sciences with an exposure to the areas of major research strength within the School. It will be taught in four three week blocks covering Mechanics, Operations research, Pure Mathematics and Statistics. Students will be required to participate proactively in the course by possible involvement in open ended problems, independent reading and mini projects.

  • General Course Information
    Course Details
    Course Code MATHS 2203
    Course Advanced Mathematical Perspectives II
    Coordinating Unit School of Mathematical Sciences
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3.5 contact hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites MATHS 1012 and MATHS 1015
    Restrictions Available to BMaSc(Adv) students only
    Course Description The aim of this course is to foster a broad appreciation of the mathematical sciences with an exposure to the areas of major research strength within the School. It will be taught in four three week blocks covering Mechanics, Operations research, Pure Mathematics and Statistics. Students will be required to participate proactively in the course by possible involvement in open ended problems, independent reading and mini projects.
    Course Staff

    Course Coordinator: Dr Andrew Black

    Other lecturers:
    A/Prof Luke Bennetts
    Dr John (Jack) Maclean
    Dr David Baraglia.

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course, students should have demonstrated:
    1.  Understanding of specialised topics in pure mathematics, dynamics, stochastics, and statistics.
    2.  Ability to create their own rigorous mathematical arguments.
    3.  Ability to work with mathematical models and analyse data.
    4.  Ability to communicate mathematics and statistics in writing.
    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.

    1

    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.

    2, 3

    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.

    4

    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.

    2, 3, 4

    Attribute 5: Intercultural and ethical competency

    Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.

    3, 4

    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.

    2
  • Learning Resources
    Required Resources
    None.
    Recommended Resources
    Course materials will be provided by the lecturers.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course is taught in four blocks of three weeks each.  There are three one-hour workshops each week for active project work under the guidance of a lecturer.
    Workload

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

    Activity Quantity Workload hours
    Workshops 36 36
    Projects 4 120
    Total 156
    Learning Activities Summary

    Each quarter of the course consists of a research project.  Students work on their projects in the workshops with guidance from the lecturer and outside the workshops independently or in informal groups.  The assessment item for each project is a report that is due on Friday of the week following the three-week block.

    Weeks 1-3.  Statistics. Evidence-based statistics: theory and practice.

    Weeks 4-6.  Stochastics.  Hidden Markov models in data science.

    Weeks 7-9.  Pure mathematics. Polynomials that fit nicely into a box.

    Weeks 10-12.  Dynamics. An introduction to asymptotic methods.

    Note.  The dynamics project takes MATHS 2101 Multivariable & Complex Calculus and MATHS 2102 Differential Equations as assumed knowledge.  The statistics project takes STATS 1005 Statistical Analysis & Modelling as assumed knowledge.  If you have not done these courses, please speak to the lecturers.  They may be able to recommend helpful background material for you to read.

  • 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 Topic Objective assessed
    Project 1 25% Pure Mathematics 1, 2, 4
    Project 2 25% Stochastics 1, 2, 3, 4
    Project 3 25% Dynamics 1, 2, 3, 4
    Project 4 25% Statistics 1, 2, 3, 4
    Assessment Related Requirements
    A mark of 50 is required to pass this course.
    Assessment Detail
    Assessment item Distributed Due
    Project report 1 Week 1 Friday of Week 4
    Project report 2 Week 4 Friday of Week 7
    Project report 3 Week 7 Friday of Week 10
    Project report 4 Week 10 Friday of Week 13
    Submission
    Reports are submitted as PDF files via MyUni.
    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 (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.

  • Student Support
  • Policies & Guidelines
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