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Advanced Environmental Computation (MAST90128)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Overview
Availability | Semester 2 |
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Fees | Look up fees |
Fitting models to data is a fundamental component of computational biology. In this subject we teach statistical and machine learning approaches, including methods specifically developed for handling spatial data. The subject will give you understanding of, and practice in, a range of modern techniques, and show how these are used in real world problems with typically available data. Topics covered include statistical learning methods for regression and classification, spatio-temporal modelling (point processes, agent-based models, spatio-temporal population simulations), spatial analyses and geographic information systems, and spatial optimisation. Diverse applications from health and ecology will be discussed and use as case studies.
The subject consists of a combination of lectures and practical classes. Lectures may take the format of a discussion session based on preliminary readings. Practical classes will consist of computer laboratory sessions. A visit to a research institution may also be organized
Intended learning outcomes
On completion of this subject, students should:
- Understand the range of available modelling methods and develop skills in selecting an approach appropriate to the task at hand
- Develop competence in computational methods relevant to regression, classification and spatial datasets
- Develop competence in evaluating model outputs
- Gain experience in using the free statistical program, R, for modelling and working with spatial data
- Develop skills in reporting analyses and evaluations
Generic skills
- In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. In particular: - computer-based data handling and statistical analysis of large data sets using the R software - ability to read, understand, modify and use computer programs for the manipulation of large data sets - time-management: completing assignments according to deadlines while making judgments about time required for different parts of the assessment
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Entry to the Master of Computational Biology
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
At least one subject (or equivalent statistical knowledge) that has given you experience in quantitative modelling, where R (the statistical program) is used.
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Additional details
- Five short answer exercises of 250 words each, testing aspects of modelling (1250 words total) held throughout the semester (25%)
- One written assignment on construction, analysis and/or evaluation of statistical or spatial models up to 1250 words in total due mid semester (25%)
- Take home examination 2500 words in the examination period (50%)
Last updated: 3 November 2022
Dates & times
- Semester 2
Coordinators Nicholas Golding and Gurutzeta Guillera-Arroita Mode of delivery On Campus (Parkville) Contact hours 2 x 1-hour lectures each week and 6 x 3-hour practical (computer laboratory) classes (45 hours in total) Total time commitment 170 hours Teaching period 29 July 2019 to 27 October 2019 Last self-enrol date 9 August 2019 Census date 31 August 2019 Last date to withdraw without fail 27 September 2019 Assessment period ends 22 November 2019
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 3 November 2022