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Advanced Environmental Computation (MAST90128)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Year of offer2019
Subject levelGraduate coursework
Subject codeMAST90128
Semester 2
FeesSubject EFTSL, Level, Discipline & Census Date

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: 21 August 2019