Those wishing to acquire machine learning skills, from a base of statistical modelling. You are likely a data scientist or aiming to become one, with expertise in probability based modelling but wish to expand your skillset into machine learning approaches.
Statistical Machine Learning
Overview
| Module Code | STAT40750 |
| Module Title | Statistical Machine Learning |
| Subject Area | Data Science |
| Credits | 5 |
| NFQ | 9 |
| EFQ | 7 |
| Start Date | 19th January 2026 |
| Duration | 12 Weeks |
| Time | N/A |
| Mode of Delivery | Online |
| Course Leader | Dr. Michael Fop |
| Fee | €900 |
| Application Deadline | 5th January 2026 |
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. Geared towards introducing students to a diverse set of techniques for analyzing complex data, this module provides an overview of a variety of fundamental statistical machine learning methods for making predictions and discovering patterns in data. Emphasis is placed on understanding, critical evaluation, and the appropriate application of these techniques in diverse real-world data analysis scenarios. Additionally, the course also guides participants on implementing these statistical learning methods through the use of the R statistical software.
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.
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
You will become familiar with popular machine learning methods for a range of data science problems. You will know which methods to use for different scenarios and how to apply them and interpret and critique results.
Video lectures posted each week that walk through module content, blending theory with examples and applications. Practice problem and coding-based problem sheets to enable self-assessment of learning outcomes. All content delivered using the VLE, which includes a monitored discussion forum with topics created for each weeks lecture material.
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.
- Familiarity with the R software for statistical computing and data programming.
Exam – 70%
Assigments – 30%
Group/class feedback, post-assessment.
Prof Cert in Data Science