STAT40590 Statistical Machine Learning

Academic Year 2019/2020

This module is a practical introduction to a collection of statistical learning methods for unsupervised and supervised learning. The aim of the course is to introduce the students to a set of techniques for the analysis of complex data, including association rules mining, clustering, classification trees, random forests, ensemble methods and support vector machines. The focus will be on the understanding, the critical evaluation and the practical and appropriate application of the different techniques.
The module will cover also how to implement these statistical learning methods using the statistical software R.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module, students should have acquired the following skills:
- Have an understanding of the theory regarding all the statistical learning methods introduced
- Being able to use the different techniques according to the context and the purpose of analysis
- Being able to evaluate the performance of the statistical learning methods introduced
- Use the statistical software R to implement these methods and being able to interpret the relevant output

Student Effort Hours: 
Student Effort Type Hours
Lectures

24

Tutorial

6

Computer Aided Lab

12

Specified Learning Activities

36

Autonomous Student Learning

102

Total

180

Approaches to Teaching and Learning:
Lectures, tutorials, computer labs, enquiry and problem-based learning. 
Requirements, Exclusions and Recommendations
Learning Requirements:

A working knowledge of statistical methods including regression analysis. Familiarity with statistical software.

Learning Recommendations:

Familiarity with the R statistical software.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Assignments Varies over the Trimester n/a Standard conversion grade scale 40% No

15

Assignment: End of trimester assignments Unspecified n/a Standard conversion grade scale 40% No

70

Project: Data analysis project Varies over the Trimester n/a Standard conversion grade scale 40% No

15


Carry forward of passed components
No
 
Resit In Terminal Exam
Autumn Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Mr. Cathal McLoughlin Tutor