Machine Learning - 2025 entry

Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bioinformatics and exploratory data analysis in natural science and engineering. This module will provide you with a thorough grounding in the theory and application of machine learning, pattern recognition, classification, categorisation, and concept acquisition.  Hence, it is particularly suitable for Computer Science, Mathematics and Engineering students and any students with some experience in probability and programming.

 

PRE-REQUISITE MODULES ECM3420 

 

In this data-driven era, modern technologies are generating massive and high-dimensional datasets. This module aims to give you an understanding of computational methods used in modern data analysis.

 

In particular, this module aims to impart knowledge and understanding of machine learning methods from basic pattern-analysis methods to state-of-the-art research topics; to give you experience of data-modelling development in practical workshops. Neural Networks, Bayesian methods and kernel-based algorithms will be introduced for extracting knowledge from large data sets of patterns (data mining techniques) where it is important to have explicit rules governing machine learning and pattern recognition. Recent development of techniques and algorithms for big-data analysis will also be addressed.

Module Specific Skills and Knowledge:

 

1.  apply advanced and complex principles for statistical machine learning to various data analysis;

 

2.  analyse novel pattern recognition and classification problems; establish statistical models for them and write software to solve them;

 

3.  apply a range of supervised and unsupervised machine learning techniques to a wide range of real-life applications.

 

Discipline Specific Skills and Knowledge:

 

4.  state the importance and difficulty of establishing a principled probabilistic model for pattern recognition;

 

5.  apply a number of complex and advanced mathematical and numerical techniques to a wide range of problems and domains.

 

Personal and Key Transferable / Employment Skills and Knowledge:

 

6.  identify the compromises and trade-offs which must be made when translating theory into practice;

 

7.  critically read and report on research papers;

 

8.  conduct small individual research projects.

 

 

Introductory material: Practical motivation for machine learning, basic ideas of supervised and unsupervised learning, classification, regression;

 

• Error and loss functions;

 

• Maximum Likelihood and Maximum a Posteriori Estimate;

 

• Bias Variance Tradeoff;

 

• Regularisation;

 

• Decision Trees and Ensemble methods;

 

• Support Vector Machines and large margin classification;

 

• Deep Neural Networks, convolutional architectures and Gradient-based optimisation;

 

• Generative Methods;

 

• Reinforcement Learning.