Computer Science

ECMM431 - From Data to Decisions (2017)

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MODULE TITLEFrom Data to Decisions CREDIT VALUE15
MODULE CODEECMM431 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS
Number of Students Taking Module (anticipated) 8
DESCRIPTION - summary of the module content

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: ECMM430 Fundamentals of Data Science
Co-requisite modules: None.

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

AIMS - intentions of the module

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.

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 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.

 

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

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
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 20 Lectures
Scheduled Learning & Teaching 12 Practical Work
Guided Independent Study 54 Coursework
Guided Independent Study 64 Background reading

 

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
Feedback on practical work 12 hours All  Oral
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 80 Written Exams 20 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
In Class Test 20 6-8 multiple-choice questions 1 Written
Individual technical report 80 3000 words 1-20 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
Written exam Written exam 1 Within 8 weeks
Individual technical report Individual technical report 2-10 Within 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 Bishop, C Pattern recognition and machine learning Springer 2007 978-0387310732 [Library]
Set Haykin, S Neural Networks: A Comprehensive Foundation 2nd Pearson 1999 000-013-908-385-3 [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]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECMM430
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Thursday 20 April 2017 LAST REVISION DATE Tuesday 10 April 2018
KEY WORDS SEARCH data science, machine learning, statistical modelling, data visualisation