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STAT312 Modelling High Dimensional Data

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An introduction to the 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.

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.

Paper title Modelling High Dimensional Data
Paper code STAT312
Subject Statistics
EFTS 0.1500
Points 18 points
Teaching period Second Semester
Domestic Tuition Fees (NZD) $904.05
International Tuition Fees (NZD) $3,954.75

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Prerequisite
(STAT 210 or STAT 241 or ECON 210 or FINC 203) and STAT 260
Restriction
STAT 242, STAT 342
Schedule C
Arts and Music, Science
Contact

Ting Wang

Teaching staff

To be confirmed

Paper Structure

Main topics

  • Penalised regression
  • Classification trees
  • Clustering
  • Dimension-reduction
  • Bagging, stacking and boosting
  • Random forests
  • Ensemble learning
Textbooks

Textbooks are not required for this paper

Graduate Attributes Emphasised
Lifelong learning, Communication, Critical thinking, Research.
View more information about Otago's graduate attributes.
Learning Outcomes

On completion of this paper, students will be able to:

  1. Describe the issues that arise when analysing high-dimensional data.
  2. Use a range of statistical learning techniques to analyse real, high-dimensional data using R
  3. Determine which is the most appropriate technique for a give research objective
  4. Interpret the results of the analysis, including any assumptions or limitations.
  5. Write a clear and succinct report on an analysis of high-dimensional data collaborator or potential client.

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Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

Lecture

Stream Days Times Weeks
Attend
A1 Wednesday 08:00-08:50 28-34, 36-41
Thursday 08:00-08:50 28-34, 36-41
Friday 08:00-08:50 28-34, 36-41

Tutorial

Stream Days Times Weeks
Attend
A1 Friday 13:00-13:50 28-34, 36-41

An introduction to the 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.

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.

Paper title Modelling High Dimensional Data
Paper code STAT312
Subject Statistics
EFTS 0.15
Points 18 points
Teaching period Second Semester
Domestic Tuition Fees Tuition Fees for 2021 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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Prerequisite
One of (ECON 210 or FINC 203 or STAT 210 or STAT 241) and STAT 260
Restriction
STAT 242, STAT 342
Schedule C
Arts and Music, Science
Contact

Matthew Parry

Teaching staff

To be confirmed, contact the department for further information

Paper Structure

Main topics

  • Penalised regression
  • Classification trees
  • Clustering
  • Dimension-reduction
  • Bagging, stacking and boosting
  • Random forests
  • Ensemble learning
Textbooks

Textbooks are not required for this paper

Graduate Attributes Emphasised
Lifelong learning, Communication, Critical thinking, Research.
View more information about Otago's graduate attributes.
Learning Outcomes

On completion of this paper, students will be able to:

  1. Describe the issues that arise when analysing high-dimensional data.
  2. Use a range of statistical learning techniques to analyse real, high-dimensional data using R
  3. Determine which is the most appropriate technique for a give research objective
  4. Interpret the results of the analysis, including any assumptions or limitations.
  5. Write a clear and succinct report on an analysis of high-dimensional data collaborator or potential client.

^ Top of page

Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

Lecture

Stream Days Times Weeks
Attend
A1 Wednesday 08:00-08:50 28-34, 36-41
Thursday 08:00-08:50 28-34, 36-41
Friday 08:00-08:50 28-34, 36-41

Tutorial

Stream Days Times Weeks
Attend
A1 Friday 13:00-13:50 28-34, 36-41