2017/18 | CEMPS Intranet | University of Exeter

MODULE TITLE	Machine Learning and Statistical Modelling	CREDIT VALUE	15
MODULE CODE	ECMM434	MODULE CONVENER	Unknown

DURATION: TERM	1	2	3
DURATION: WEEKS

Number of Students Taking Module (anticipated)	20

DESCRIPTION - summary of the module content

Modern data analysis draws on developments from both machine learning and statistical modelling. Applications include, for example, image and speech analysis, medical imaging, bioinformatics and the analysis of data from natural science, engineering, health, government and industry. Building on what you learned in the pre-requisite module ECMM431 From Data to Decisions this module will provide you with a thorough grounding in both the theory and application of machine learning and statistical modelling, including clustering, classification, pattern recognition, feature extraction and concept acquisition.

Pre-requisite modules: ECMM431 From Data to Decisions.
Co-requisite modules: None.

AIMS - intentions of the module

This module aims to provide you with a set of fundamental tools in machine learning and statistical modelling. It will provide a grounding in the underlying statistical theory and a solid understanding of the algorithms required for their application. You will apply the material learnt within the course to data analysis problems during the workshops.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

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

Module Specific Skills and Knowledge

1. Understand the reasoning behind the choice of methods, in machine learning and statistical modelling, when performing complex data analysis;
2. Apply a range of supervised and unsupervised machine learning and statistical techniques to a wide range of real-life applications;

3. Perform novel data analyses, including pattern recognition, regression and classification problems, by understanding the underlying principles behind different methods to a level that allows you to develop software applying them to complex datasets.

Discipline Specific Skills and Knowledge

4. State the importance and difficulty of establishing a principled probabilistic model for pattern recognition;
5. Apply advanced mathematical and computational 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, notably when dealing with ‘big data’;
7. Read and critically assess research papers;

8. Conduct small individual research projects.

SYLLABUS PLAN - summary of the structure and academic content of the module

Topics will include:

Generalised Linear Models (GLM) and Generalised Additive Models (GAM)
Generative and discriminative models
Ensemble methods: Random Forests & Boosting.
Model assessment, including Receiver Operating Characteristic (ROC) analysis and simulation methods.
Bayesian hierarchical modelling, Hidden Markov Models.
Latent variables.
Introduction to Gaussian Processes.

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	32.00	Guided Independent Study	118.00	Placement / Study Abroad	0.00

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of study time	Description
Scheduled Learning & Teaching activities	16	Lectures
Scheduled Learning & Teaching activities	16	Workshops/practicals
Guided independent study	34	Project and coursework
Guided independent study	88	Background reading and coursework preparation

ASSESSMENT

FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Workshops / practicals	16 hours	All	Oral

SUMMATIVE ASSESSMENT (% of credit)

Coursework	100	Written Exams	0	Practical Exams	0

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Coursework	100	2000-3000 words	All	Written

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original Form of Assessment	Form of Re-assessment	ILOs Re-assessed	Time Scale for Re-assessment
Coursework	Coursework	All	Wtihin 8 weeks

RE-ASSESSMENT NOTES

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%.

RESOURCES

INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

ELE: http://vle.exeter.ac.uk/

Web based and Electronic Resources:

Other Resources:

Reading list for this module:

Type	Author	Title	Edition	Publisher	Year	ISBN	Search
Set	Shawe-Taylor, J. and Cristianini, N.	Kernel methods for pattern analysis		Cambridge University Press	2006	521813972	[Library]
Set	Bishop, C	Pattern recognition and machine learning		Springer	2007	978-0387310732	[Library]
Set	Webb, A	Statistical Pattern Recognition	2	Wiley	2002	0-470-84513-9	[Library]
Set	Hastie, T., Tibshirani, R. & Friedman, J.	The Elements of Statistical Learning: Data Mining, Inference, and Prediction.	2nd	Springer	2009		[Library]
Set	Kevin Murphy	Machine Learning: A Probabilistic Perspective	1st	MIT Press	2012	978-0-262-018029	[Library]
Set	Barber, D.	Bayesian Reasoning and Machine Learning		Cambridge University Press	2012	978-0-521-51814-7	[Library]
Set	Rasmussen, C.E. and Williams C.K.I.	Gaussian Processes for Machine Learning		Cambridge, MA: MIT Press.	2006	0-262-18253-X	[Library]

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	ECMM431
CO-REQUISITE MODULES

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Thursday 20 April 2017	LAST REVISION DATE	Monday 10 July 2017

KEY WORDS SEARCH	Machine learning, statistical modelling

Computer Science

ECMM434 - Machine Learning and Statistical Modelling (2017)