Machine Learning and Statistical Modelling - 2019 entry
MODULE TITLE | Machine Learning and Statistical Modelling | CREDIT VALUE | 15 |
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MODULE CODE | ECMM434 | MODULE CONVENER | Unknown |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS |
Number of Students Taking Module (anticipated) | 20 |
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***DATA SCIENCE AND DATA SCIENCE WITH BUSINESS STUDENTS ONLY***
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.
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.
On successful completion of this module you should be able to:
Module Specific Skills and Knowledge
2. Apply a range of supervised and unsupervised machine learning and statistical techniques to a wide range of real-life applications;
Discipline Specific Skills and Knowledge
5. Apply advanced mathematical and computational techniques to a wide range of problems and domains.
Personal and Key Transferable / Employment Skills and Knowledge
7. Read and critically assess research papers;
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.
Scheduled Learning & Teaching Activities | 32 | Guided Independent Study | 118 | Placement / Study Abroad | 0 |
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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 |
Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Workshops / practicals | 16 hours | All | Oral |
Coursework | 100 | Written Exams | 0 | Practical Exams | 0 |
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Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Coursework | 100 | 2000-3000 words | All | Written |
Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-assessment |
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Coursework | Coursework | All | Wtihin 8 weeks |
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%.
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 |
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Set | Shawe-Taylor, J. and Cristianini, N. | Kernel methods for pattern analysis | Cambridge University Press | 2006 | 521813972 | |
Set | Christopher Bishop | Pattern Recognition and Machine Learning | Springer | 2007 | 978-0387310732 | |
Set | Webb, A. | Statistical Pattern Recognition | 2 | Wiley | 2002 | 0-470-84513-9 |
Set | Hastie, T., Tibshirani, R. & Friedman, J. | The Elements of Statistical Learning: Data Mining, Inference, and Prediction | 2nd | Springer | 2009 | 978-0387848570 |
Set | Murphy, K. | Machine Learning: A Probabilistic Perspective | 1st | MIT Press | 2012 | 978-0-262-018029 |
Set | David Barber | Bayesian Reasoning and Machine Learning | Cambridge University Press | 2012 | 978-0-521-51814-7 | |
Set | Rasmussen, C.E. and Williams C.K.I. | Gaussian Processes for Machine Learning | Cambridge, MA: MIT Press. | 2006 | 0-262-18253-X |
CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
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PRE-REQUISITE MODULES | ECMM431 |
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CO-REQUISITE MODULES |
NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Tuesday 10th July 2018 | LAST REVISION DATE | Tuesday 18th December 2018 |
KEY WORDS SEARCH | Machine learning, statistical modelling |
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Please note that all modules are subject to change, please get in touch if you have any questions about this module.