# STAT40750 Statistical Machine Learning (online)

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.

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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

Online Learning

35

Total

120

Approaches to Teaching and Learning:
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.
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.
- 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, STAT30270 - Statistical Machine Lrng

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 Yes Other 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

How will my Feedback be Delivered?

Not yet recorded.

James, Witten, Tibshirani, Hastie - An Introduction to Statistical Learning with Applications in R - https://www.statlearning.com/

Hastie, Tibshirani, Friedman - The Elements of Statistical Learning - https://hastie.su.domains/ElemStatLearn/

Bishop - Pattern Recognition and Machine Learning - https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/
Name Role
Mr Brian Buckley Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.

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