Introductory description

Data Mining.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Introduction to machine learning, basic concepts and motivation;
Data pre-processing and basic data transformations;
Regression models (linear regression, logistical regression);
Classification: linear discriminants, SVMs, other classifiers such as decision trees and forests;
Model evaluation, bias-variance trade-off;
Ensemble methods: boosting, bagging & random forests;
Dimensionality reduction: Principal Component Analysis (PCA), T-distributed Stochastic Neighbour Embedding (t-SNE/UMAP);
Introduction to deep learning, backpropagation, gradient descent;
Convolutional neural networks;
Word embeddings;
Sequence-to-sequence models, Residual Neural Networks;
Attention mechanisms and memory networks;
Language processing;
Unsupervised deep learning and generative models;
Learning Strategies such as Transfer learning, etc.

Learning outcomes

By the end of the module, students should be able to:

• Display a comprehensive understanding of different data mining tasks and the algorithms most appropriate for addressing them.
• Evaluate models/algorithms with respect to their accuracy.
• Demonstrate capacity to perform a self-directed piece of practical work that requires the application of data mining techniques.
• Critique the results of a data mining exercise.
• Develop hypotheses based on the analysis of the results obtained and test them.
• Conceptualise a data mining solution to a practical problem.

Indicative reading list

Please see Talis Aspire link for most up to date list.

View reading list on Talis Aspire

Research element

The students shall be required to explore the literature about latest methods related to classification and deep learning.

Interdisciplinary

Applied machine learning lies at the intersection of statistics, data mining, computer science and mathematics.

Subject specific skills

Design of machine learning solutions
Learning to develop novel algorithms related to machine learning
Conducting proper experiment design in machine learning

Transferable skills

Experiment design
Critical Thinking
How to conduct literature reviews