STAT4064 Applied Predictive Modelling

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 2UWA (Perth)Face to face
Details for undergraduate courses
  • Honours option in Mathematics and Statistics [Bachelor of Science (Honours)]
Content
This unit covers a set of tools for modelling, understanding and predicting from complex data sets. The tools are selected from topics that are a natural blend of statistics and machine learning, and are motivated and demonstrated with applied examples. The underlying general concepts and basic theory are discussed at a level accessible to students. Data sets are analysed using the statistical package R and the unit provides an introduction to this software. Topics are selected from statistical inference, linear regression, model selection, classification, resampling methods, tree-based methods, support vector machines and machine learning.
Outcomes
Students are able to (1) apply appropriate techniques from the above topics to real world data and communicate results in a logical and coherent fashion; (2) apply statistical reasoning in general to analyse the essential structure of problems in various fields of data science; (3) extend students' knowledge of statistical modelling techniques and adapt known solutions to different situations; and (4) undertake continuous learning in statistical predictive modelling and inference, being aware that an understanding of fundamentals is necessary for effective application.
Assessment
Typically this unit is assessed in the following ways: (1) tests and assignments and (2) a final examination. Further information is available in the unit outline.

Supplementary assessment is not available in this unit.
Unit Coordinator(s)
Dr Nazim Khan
Unit rules
Prerequisites:
STAT2401 Analysis of Experiments
or
STAT2402 Analysis of Observations
or
STAT2062 Fundamentals of Probability with Applications
Advisable prior study:
STAT2062 Fundamentals of Probability with Applications
or
(STAT2401 Analysis of Experiments
and
STAT2402 Analysis of Observations)
Contact hours
lectures: 2 hours per week; computer laboratories: 2 hours per week
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