Skip to main content
Unit of study_

STAT3888: Statistical Machine Learning

Data Science is an emerging and inherently interdisciplinary field. A key set of skills in this area fall under the umbrella of Statistical Machine Learning methods. This unit presents the opportunity to bring together the concepts and skills you have learnt from a Statistics or Data Science major, and apply them to a joint project with NUTM3888 Metabolic Cybernetics where Statistics and Data Science students will form teams with Nutrition students to solve a real world problem using Statistical Machine Learning methods. The unit will cover a wide breadth of cutting edge supervised and unsupervised learning methods will be covered including principal component analysis, multivariate tests, discrimination analysis, Gaussian graphical models, log-linear models, classification trees, k-nearest neighbours, k-means clustering, hierarchical clustering, and logistic regression. In this unit, you will continue to understand and explore disciplinary knowledge, while also meeting and collaborating through project-based learning; identifying and solving problems, analysing data and communicating your findings to a diverse audience. All such skills are highly valued by employers. This unit will foster the ability to work in an interdisciplinary team, and this is essential for both professional and research pathways in the future.

Code STAT3888
Academic unit Mathematics and Statistics Academic Operations
Credit points 6
STAT2X11 and (DATA2X02 or STAT2X12)
STAT3914 or STAT3014
Assumed knowledge:
STAT3012 or STAT3912 or STAT3022 or STAT3922

At the completion of this unit, you should be able to:

  • LO1. apply disciplinary knowledge in statistics and data science to solve problems in an interdisciplinary context (nutrition)
  • LO2. find, define, and delimit authentic problems in order to address them
  • LO3. create an investigation strategy, explore solutions, discuss approaches, and predict outcomes
  • LO4. apply, formulate, interpret, and compare statistical machine learning methods including (wherever relevant) evaluation of model appropriateness
  • LO5. demonstrate integrity, confidence, personal resilience, and the capacity to manage challenges, both individually and in teams
  • LO6. collaborate with diverse groups across cultural and disciplinary boundaries to develop solution(s) to the project problems
  • LO7. communicate project outcomes effectively to a broad audience
  • LO8. identify appropriate machine learning problems to a particular problem, and judge the appropriateness of model evaluation procedures.