Module Code - Title:

MA5002 - STATISTICAL LEARNING WITH APPLICATIONS

Year Last Offered:

2025/6

Hours Per Week:

Lecture

2

Lab

1

Tutorial

0

Other

3

Private

4

Credits

6

Grading Type:

N

Prerequisite Modules:

Rationale and Purpose of the Module:

To ground the students in applied statistical learning techniques used in data analytics. The module introduces state-of-the-art supervised and unsupervised learning techniques such as classification methods, clustering methods, and dimension reduction methods. The students will learn how to implement these techniques in R with practical applications.

Syllabus:

1. Introduction to multivariate data. 2. Classification methods (linear and quadratic, k-NN, SVM). 3. Regression trees. 4. Dimension reduction (PCA, factor analysis). 5. Cluster analysis (hierarchical, k-means, model-based). 6. Implementation of these methods in R.

Learning Outcomes:

Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

On successful completion of this module, students will be able to: 1. Recognise the underlying structure of complex multivariate data sets in real-world applications. 2. Identify the most appropriate statistical learning method to implement in a wide variety of settings. 3. Critically appraise and demonstrate an understanding of the strengths and limitations of each method. 4. Apply each method to real-world data using R and RStudio. 5. Compile data analysis reports, interpreting the results and presenting findings to key stakeholders in industrial or commercial settings.

Affective (Attitudes and Values)

On successful completion of this module, students will be able to: 1. Display sharp critical appraisal skills with an appreciation of the appropriate analytics tools to use in a variety of real-world applications. 2. Formulate a well-constructed rationale to defend and justify any decisions made. 3. Display a professional commitment to challenge incorrect applications of methods.

Psychomotor (Physical Skills)

NA

How the Module will be Taught and what will be the Learning Experiences of the Students:

This module will be block taught online as part of the UL@Work Human Capital Initiative. This module will contribute towards students who are KNOWLEDGEABLE (being able to bring their discipline knowledge to bear on real world problems), RESPONSIBLE (being able to challenge and question the appropriate use of data and statistical learning methods), PROACTIVE (making active use of data to drive improvements and positive change), ARTICULATE (being able to report their findings to a variety of stakeholders).

Research Findings Incorporated in to the Syllabus (If Relevant):

Prime Texts:

James, G., Witten, D., Hastie, T., Tibshirani, R (2018) An Introduction to Statistical Learning with Applications in R , Springer

Other Relevant Texts:

Hastie, T., Tibshirani, R., Friedman, J. (2008) The Elements of Statistical Learning: Data Mining, Inference and Prediction , Springer

Programme(s) in which this Module is Offered:

Semester(s) Module is Offered:

Spring

Module Leader:

Philippa.Wilkes@ul.ie