# STAT30270 Statistical Machine Learning

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
Lectures

24

Computer Aided Lab

11

Specified Learning Activities

25

Autonomous Student Learning

60

Total

120

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

- Basic knowledge of linear algebra (vectors, vector spaces, matrices), calculus (derivatives), and function optimization.
- Basic understanding of statistical inference (confidence intervals, hypothesis testing, etc.) and familiarity with common probability distributions.
- Basic knowledge of regression analysis and linear models, including multiple linear regression.
- Familiarity with the R software for statistical computing and data programming.

Learning Recommendations:

- Students should have a knowledge of statistical inference at a level equivalent to that which would be achieved upon completion of "Inferential Statistics STAT20100" or "STAT30280 Inference for Data Analytics (Onl)", or modules with similar contents and learning outcomes.
- Students should have a knowledge of data programming and analysis at a level equivalent to that which would be achieved upon completion of "Data Programming with R STAT40620", and/or modules with a relevant component of coding and implementation of statistical methods with R.
- Knowledge of regression analysis and linear models to a level equivalent to that of "STAT20230 Modern Regression Analysis" or "STAT20240 Predictive Analytics" is beneficial.

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 Alternative linear conversion grade scale 40% No

30

Examination: End of trimester written exam 2 hour End of Trimester Exam No Alternative linear conversion grade scale 40% No

70

Carry forward of passed components
No

Resit In Terminal Exam
Summer Yes - 2 Hour
Feedback Strategy/Strategies

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

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Mr Brian Buckley Tutor
Mr Brian Hassett Tutor
Iuliia Promskaia Tutor
Niyati Seth Tutor