Machine Learning (Professional) - 2020 entry

*** This module is a “professional” version of the similar module ECMM422. It is intended to be taught in a short-fat format based around 3-day teaching blocks, as part of the MSc Data Science (Professional) programme. ***

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.

Pre-Requisite module: ECMM431

Co-requisite modules: None.
 

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.

On successful completion of this module you should be able to:

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.

Topics will include:

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

- Describing data;

- Latent descriptions: k-means, maximum likelihood; mixture models; PCA; ICA;

- Unsupervised learning: Clustering; Locality Sensitive Hashing;

- Supervised models: k-nearest neighbours, linear and non-linear regression, linear discriminant analysis, logistic regression, SVM and maximum margin classifiers;

- Loss functions and maximum likelihood estimators;

- Bayesian learning & sampling;

- Neural nets and deep learning;

- Evaluation of performance, dataset balance;

- Ensemble methods: boosting, bagging, decision trees and random forests Metric learning;

- Markov decision processes: Reinforcement learning (Q-learning).
 

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%

Reading list for this module: