Overview
Statistical learning techniques commonly used to analyse high-dimensional (or multivariate) data. Penalised regression, classification trees, clustering, dimension-reduction, bagging, stacking, boosting, random forests and ensemble learning.
Statistical learning techniques commonly used to analyse high-dimensional (or multivariate) data. Principal component analysis, clustering, dimensionality reduction, classification methods, tree-based methods, penalised regression.
The aim of this paper is to introduce students to many of the statistical learning techniques that are now used to analyse high-dimensional data. Students will learn the underlying rationale for each method and gain practice in using it on real data in R.
About this paper
| Paper title | Statistical Learning |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,315.10 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- STAT 401 or (STAT 260 and STAT 270 and STAT 310), or equivalent (contact department for further information)
- Restriction
- STAT 312
- Contact
- Teaching staff
- Paper Structure
Main topics:
- Principal component analysis.
- Exploratory factor analysis.
- Clustering methods.
- Dimensionality reduction.
- Classification methods.
- Tree-based methods.
- Penalised regression.
- Multiple testing.
- Textbooks
Textbooks are not required for this paper.
- Graduate Attributes Emphasised
Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Research.
View more information about Otago's graduate attributes.- Learning Outcomes
Students who successfully complete the paper will be able to:
- Describe the issues that arise when analysing high-dimensional data.
- Use a range of statistical learning techniques to analyse real, high-dimensional data using R.
- Determine which is the most appropriate technique for a given research objective.
- Interpret the results of the analysis, including any assumptions or limitations.
- Write a clear and succinct report on an analysis of high-dimensional data for a collaborator or potential client.
- Give an oral presentation of their results.