Computer Science

ECMM434 - Machine Learning and Statistical Modelling (2019)

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MODULE TITLEMachine Learning and Statistical Modelling CREDIT VALUE15
MODULE CODEECMM434 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS
Number of Students Taking Module (anticipated) 20
DESCRIPTION - summary of the module content

***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.

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 Tuesday 10 July 2018 LAST REVISION DATE Tuesday 18 December 2018
KEY WORDS SEARCH Machine learning, statistical modelling