MATHS 2203 - Advanced Mathematical Perspectives II
North Terrace Campus - Semester 2 - 2019
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General Course Information
Course Details
Course Code MATHS 2203 Course Advanced Mathematical Perspectives II Coordinating Unit 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 Assessment Ongoing assessment Course Staff
Course Coordinator: Dr Michael Chen
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
- Develop appreciation for the distinction between "good" and "bad" mathematics.
- Develop appreciation of the importance of classification in mathematics.
- Understand the connections between probability, statistical data and evidence.
- Be able to apply probabilistic reasoning in real contexts involving data and evidence.
- Be able to analyse simple difference equation and Markov chain models.
- Be able to develop simple mathematical models.
- Be able to analyse differential equations, and appreciate their application to real-world problems.
- Be able to use asymptotic techniques to solve algebraic and differential equations.
- Be able to write project reports.
最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog 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)
1,2,3,4,5,6,7 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
1,2,3,4,5,6,7 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
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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. In each block there are 9 lectures and 1 tutorial.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Lecture 36 108 Project/Assignment 4 48 Total 156 Learning Activities Summary
Pure Mathematics
Week 1: An overview of perspectives in mathematics, particularly as they apply to mathematics at The 最新糖心Vlog of Adelaide.
Week 2: Separation, tessellation and triangulation.
Week 3: The classification of closed surfaces.Applied Mathematics I: Stochastic Modelling and Operations Research
Week 4: Introduction to mathematical modelling - the process and its objectives. Thinking about simple problems, and turning words into equations. Linear difference equations - definitions and methods of solution. More complicated problems, and getting to grips with data.
Week 5: Reflecting on the model. How good a fit is our model to the data? What is does it fail to take into account? Analytical methods for understanding the behaviour of diff erence equation models: cob-webbing, stability of a fixed point. A more detailed look at the discrete logistic equation - fixed points, periodic cycles, chaos, the idea of a bifurcation.
Week 6: Introduction to discrete-time Markov chains. Development of a simple model (a stochastic logistic model). Transition probability matrices. Analytic evaluation of mean and variance. Ability to explain variability in data.
Statistics
Week 7: Probability, coincidence and evidence. Review of basic probability axioms and properties. Bayes’ Theorem and updating information. Decision rules.
Week 8: Tests of statistical hypotheses, type 1 and type 11 error probabilities, the Neyman-Pearson Lemma. The power function. P-values as evidence and the posterior interpretation.
Week 9: Probabilistic evidence in legal and forensic studies, and the prosecutor’s fallacy.
Applied Mathematics II: Dynamics, Modelling and Computation
Week 10: Further complex calculus and applications (fluids, solids, electrostatics).
Week 11: How do planes fly? Introduction to asymptotic methods.
Week 12: Asymptotic solution of differential equations, including boundary layer problems. -
Assessment
The 最新糖心Vlog'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.
Assessment Summary
Component Weighting Topic Objective assessed Project/Assignment 1 25% Pure Mathematics 1, 2, 9 Project/Assignment 2 25% Applied Mathematics I 5, 6, 9 Project/Assignment 3 25% Statistics 3, 4, 9 Project/Assignment 4 25% Applied Mathematics II 7, 8, 9 Assessment Related Requirements
An aggregrate score of 50% or more is required to pass this course.Assessment Detail
Assessment item Distributed Due Project/Assignment 1 Weeks 1-3 TBC Project/Assignment 2 Weeks 4-6 TBC Project/Assignment 3 Weeks 7-9 TBC Project/Assignment 4 Weeks 10-12 TBC Submission
Reports are submitted to the relevant lecturer in either paper format (with a signed cover sheet) or electronically or a combination of both formats. Late assignments are not accepted.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 .
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Student Feedback
The 最新糖心Vlog 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 最新糖心Vlog 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.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
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