Useful links
Unit of study_
OSTA5003: Computational Statistical Methods
2024 unit information
The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical learning, inference, exploratory data analysis and data mining. Advanced computational methods for statistical learning will be introduced, including clustering, density estimation, smoothing, predictive models, model selection, combinatorial optimisation methods, sampling methods, the Bootstrap and Monte Carlo approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice.
Unit details and rules
Managing faculty or University school:
Mathematics and Statistics Academic Operations
| Code | OSTA5003 |
|---|---|
| Academic unit | Mathematics and Statistics Academic Operations |
| Credit points | 6 |
|
Prerequisites:
?
|
None |
|---|---|
|
Corequisites:
?
|
None |
| Prohibitions:
?
|
None |
| Assumed knowledge:
?
|
STAT5002 or equivalent introductory statistics course with a statistical computing component |
At the completion of this unit, you should be able to:
- LO1. Formulate domain/context specific questions and identify appropriate statistical analysis.
- LO2. Formulate, evaluate and interpret appropriate statistical models to describe the relationships between multiple factors.
- LO3. Perform statistical machine learning using a given classifier and create a cross-validation scheme to calculate the prediction accuracy.
- LO4. Understand, perform and interpret various unsupervised machine learning methods
- LO5. Construct and implement resampling techniques to understand the behaviour of statistical models.
- LO6. Create a reproducible report to communicate outcomes using a programming language.
Unit availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
|
Semester 1b 2024
|
Online | Online Program | |
|
Semester 2b 2024
|
Online | Online Program |
Outline unavailable
|
Modes of attendance (MoA)
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.
