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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.

    About this paper

    Paper title Modelling High Dimensional Data
    Subject Statistics
    EFTS 0.15
    Points 18 points
    Teaching period Semester 2 (On campus)
    Domestic Tuition Fees ( NZD ) $981.75
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    One of (ECON 210 or FINC 203 or STAT 210 or STAT 241) and STAT 260
    STAT 242, STAT 342, STAT 425
    Schedule C
    Arts and Music, Science

    Teaching staff

    Dr Matthew Parry

    Paper Structure

    The main topics of this paper are:

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

    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


    Semester 2

    Teaching method
    This paper is taught On Campus
    Learning management system


    Stream Days Times Weeks
    A1 Monday 12:00-12:50 29-35, 37-42
    Thursday 12:00-12:50 29-35, 37-42
    Friday 13:00-13:50 29-35, 37-42


    Stream Days Times Weeks
    Attend one stream from
    A1 Tuesday 14:00-14:50 29-35, 37-42
    A2 Friday 15:00-15:50 29-35, 37-42
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