*** This module is a “professional” version of the similar module ECMM445. 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. ***

One of the primary aims of data science is to effectively use data to make better decisions. This module will introduce you to machine learning and statistical methods for learning from data. You will learn about the principal learning paradigms from a theoretical point of view and gain practical experience through a series of workshops. Throughout the module, there will be an emphasis on dealing with real data, and you will use, modify and write software to implement learning algorithms. It is often useful to be able to visualise data and you will gain experience of methods of reducing the dimension of large datasets to facilitate visualisation and understanding.

Pre-requisite modules: ECMM456 Fundamentals of Data Science (Professional)

Co-requisite modules: None.

This module is a core module for MSc Data Science (Professional).

This module aims to provide you with some of the main ideas of machine learning and statistical modelling in a data science context. It will provide a grounding in the theory and application of some machine learning methods for classification, regression, and unsupervised learning including clustering and dimensionality reduction. We will also discuss the details of specific methods for classification, clustering, and for visualising complex datasets.

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

Module Specific Skills and Knowledge

1. Understand concepts of supervised and unsupervised learning and different methodologies for applying machine learning and statistical modelling in each case;

2. Able to pre-process data to make it suitable for analysis;

3. Apply simple supervised and unsupervised pattern recognition and machine learning techniques to solve a wide range of problems;

4. Analyse novel pattern recognition and classification problems, establish models for them and write software to solve them.

Discipline Specific Skills and Knowledge

5. Understand different approaches to problem-solving in data science;

6. State the importance and difficulty of establishing principled models for pattern recognition;

7. Use Python and R for scientific analysis and simulation of real data.

Personal and Key Transferable / Employment Skills and Knowledge

8. Identify the compromises and trade-offs that must be made when translating theory into practice;

9. Critically read and assess research papers;

10. Conduct small individual research projects.

Topics (with associated exercises and seminar discussions):

Taxonomy of problems and approaches in machine learning and statistical modelling

Data description and pre-processing

Probabilistic classification

Clustering and dimension reduction

Linear and logistic statistical models

Model assessment, cross-validation, hypothesis Testing

Bayesian learning

Linear support vector machines

Clustering (hierarchical and partitional)

Principal component analysis
 

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%.

Basic reading:

ELE: http://vle.exeter.ac.uk/
 
Web based and Electronic Resources:

Other Resources:
 

Reading list for this module: