APP MTH 4046 - Applied Mathematics Topic A - Honours
North Terrace Campus - Semester 1 - 2021
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General Course Information
Course Details
Course Code APP MTH 4046 Course Applied Mathematics Topic A - 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 Honours students only 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 .
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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)
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
all 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
Access to the internet.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.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload Hours Lecture classes 30 108 Assignments 4 48 Total 156
Learning Activities Summary
- Monte Carlo methods (weeks 1-4)
- Stochastic differential equation models with financial applications (weeks 5-6)
- Continuous-time Markov chain models (week 6)
- Markov chain Monte Carlo (week 7)
- Learning and inference for dynamical systems (weeks 8-12)
Specific Course Requirements
None. -
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 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.Submission
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:
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
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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