STAT30270 Statistical Machine Learning

Academic Year 2021/2022

Statistical Machine Learning encompasses a collection of techniques for discovering patterns in data and making predictions, involving models and methods at the intersection of Machine Learning and Statistics. With the aim of introducing the students to a set of techniques for the analysis of complex data, this module provides an overview of a variety of statistical learning methods for unsupervised and supervised learning. Focus will be placed on the understanding, the critical evaluation and the appropriate application of the different techniques in different data analysis scenarios.

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

Indicative Module Content:

Unsupervised learning:
- Association rule analysis
- Clustering

Supervised learning:
- Logistic regression for classification
- Classification trees
- Ensemble methods
- Support vector machines
- Evaluation of classifiers, model selection, and tuning

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities

25
Autonomous Student Learning

60
Lectures

24
Computer Aided Lab

11
Total

120
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 the R software for statistical computing and data programming.


Module Requisites and Incompatibles
Incompatibles:
FIN30520 - Machine Learning Finance


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Homework assignments, code-based exercises, data analysis tasks Varies over the Trimester n/a Other No

30
Examination: End of trimester written exam 2 hour End of Trimester Exam No Other No

70

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

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
      
Lecture Offering 1 Week(s) - 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32 Fri 11:00 - 11:50
Lecture Offering 1 Week(s) - 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32 Wed 15:00 - 15:50
Computer Aided Lab Offering 1 Week(s) - 20, 21, 22, 23 Thurs 17:00 - 17:50
Computer Aided Lab Offering 1 Week(s) - 24, 25, 29, 32 Thurs 17:00 - 17:50
Computer Aided Lab Offering 1 Week(s) - 28, 30, 31 Thurs 17:00 - 17:50
Computer Aided Lab Offering 2 Week(s) - 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32 Wed 17:00 - 17:50
Computer Aided Lab Offering 3 Week(s) - 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32 Tues 17:00 - 17:50
Spring