MATHS 2203 - Advanced Mathematical Perspectives II
North Terrace Campus - Semester 2 - 2022
General Course Information
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 Coordinator: Dr Andrew BlackOther lecturers:
A/Prof Luke Bennetts
Dr John (Jack) Maclean
Dr David Baraglia.
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn 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.
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.
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.
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.
Recommended ResourcesCourse materials will be provided by the lecturers.
Learning & Teaching Activities
Learning & Teaching ModesThe 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.
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.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
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 RequirementsA mark of 50 is required to pass this course.
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
SubmissionReports are submitted as PDF files via MyUni.
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.
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.
- Academic Support with Maths
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- Student Life Counselling Support - Personal counselling for issues affecting study
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- Reasonable Adjustments to Teaching & Assessment for Students with a Disability Policy
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- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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