MIS41120 Statistical Learning

Academic Year 2023/2024

Broadly speaking, we think of Statistical and Machine Learning as computational methods that use (learn from) experience to improve performance or prediction accuracy. They arose in different research communities but have significant overlap. Statistical Learning focusses more on linear models, for which there is stronger theoretical foundation, and (to an extent) on inference; Machine Learning focusses more on nonlinear methods, founded more on experimental evidence, and is often more associated with prediction.
This Statistical Learning course discusses these, and also investigates the foundations of these methods: how well they work, error estimates, tradeoffs involved, etc: the principles underpinning algorithmic learning - the methods used in Knowledge Discovery and Data Mining.
Statistical learning refers to supervised and unsupervised learning, especially regression, classification, clustering, and especially with structured numerical data. These are the most common techniques used for modelling, with the goals of inference and prediction in business (and elsewhere); hence, their statistical theory is well-developed.
This module aims to develop both theory and practice to expert level.

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Curricular information is subject to change

What will I learn?

Learning Outcomes:

On completion of the module students should be able to:
● Distinguish between supervised and unsupervised learning and define regression, classification and clustering problems formally;
● Describe bias, variance and the bias-variance trade-off;
● Describe common loss functions and performance measures;
● Define the problem of overfitting and how to overcome it;
● Distinguish among common models, from linear regression to artificial neural networks to generalised linear models, and execute them with the help of a software library;
● Describe the main ideas of statistical learning theory, including the theory of the VC dimension.

Indicative Module Content:

Topics of the course are drawn from:
● Motivation: goals of prediction and inference/understanding
● Supervised and unsupervised learning: Regression, Classification, Clustering
● Measuring performance: accuracy and interpretability
● Bias, variance and the bias-variance tradeoff
● Generalisation and stability
● Model selection
● Loss functions
● The problem of Overfitting: Regularisation
● Sparse models including the lasso, elastic net and support vector machine
● Generalised linear models
● Artificial neural networks
● Deep nets
● Model capacity, shattering and VC dimension

How will I learn?

Student Effort Type	Hours
Specified Learning Activities	40
Autonomous Student Learning	100
Lectures	36
Total	176

Am I eligible to take this module?

Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles

Not applicable to this module.

How will I be assessed?

Assessment Strategy

Description	Timing	Open Book Exam	Component Scale	Must Pass Component	% of Final Grade
Assignment: Project work on data analysis	Throughout the Trimester	n/a	Graded	No	25
Examination: Main Examination	2 hour End of Trimester Exam	No	Standard conversion grade scale 40%	No	75

Carry forward of passed components
Yes

What happens if I fail?

Resit In	Terminal Exam
Autumn	Yes - 2 Hour

Assessment feedback

Feedback Strategy/Strategies

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

How will my Feedback be Delivered?

Feedback on strengths and weaknesses of assignment submission

Associated Staff

Name	Role
Assoc Professor Peter Keenan	Lecturer / Co-Lecturer

When is this module offered?


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