APP MTH 4048 - Applied Mathematics Topic C - Honours
North Terrace Campus - Semester 1 - 2017
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General Course Information
Course Details
Course Code APP MTH 4048 Course Applied Mathematics Topic C - Honours Coordinating Unit Mathematical Sciences Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 2.5 hours per week Available for Study Abroad and Exchange Y Restrictions May only be presented towards some Engineering Programs Assessment Ongoing assessment 30%, exam 70% Course Staff
Course Coordinator: Dr Andrew Smith
This is the same course as APP MTH 7044 - Applied Mathematics Topic CCourse Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
In 2017, the topic of this course will be Stochastic Processes for Population and Epidemic Modelling.
Synopsis
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 formUniversity 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)
all 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
all 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
7 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
4,7 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
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Learning Resources
Required Resources
None.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.Workload
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:
- 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 Objective Assessed Exam 70% all Assignments 30% all Assessment Related Requirements
An aggregate score of 50% or more is required to pass the course.Assessment Detail
There will be three assignments worth 30% of the total mark. The remaining 70% will come from the exam.Submission
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:
M11 (Honours Mark Scheme) Grade Grade reflects following criteria for allocation of grade Reported on Official Transcript Fail A mark between 1-49 F Third Class A mark between 50-59 3 Second Class Div B A mark between 60-69 2B Second Class Div A A mark between 70-79 2A First Class A mark between 80-100 1 Result Pending An interim result RP Continuing Continuing CN 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 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.
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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
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