Computer Science

ECMM445 - Learning from Data (2019)

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MODULE TITLELearning from Data CREDIT VALUE15
MODULE CODEECMM445 MODULE CONVENERDr Hugo Barbosa (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11 0 0
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content

Artificially intelligent machines and software must assimilate data from their environment and make decisions based upon it. Likewise, we live in a data-rich society and must be able to make sense of complex datasets. This module will introduce you to machine learning 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.

The module will also cover some recent neural network architectures and related learning algorithms.

AIMS - intentions of the module

This module aims to equip you with the fundamentals of machine learning and at the same time discuss technical aspects of some well-known machine learning models and related learning algorithms. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods (clustering and dimension reduction). The module will cover kernel methods and neural networks (feed-forward architectures only).

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. apply principles for statistical and neural pattern recognition to novel data.
2. analyse novel pattern recognition and classification problems, establish models for them and write software to solve them.
 

Discipline Specific Skills and Knowledge

3. utilise a range of supervised and unsupervised pattern recognition and machine learning techniques to solve a wide range of problems.
4. state the importance and difficulty of establishing principled models for pattern recognition.
 

Personal and Key Transferable / Employment Skills and Knowledge

5. use python and modern machine learning libraries for scientific analysis and simulation.
6. identify the compromises and trade-offs that must be made when translating theory into practice.
7. critically read and report on research papers.

 

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

- introductory material: basic ideas of classification, regression and preliminary concepts;

- data description;

- statistical preliminaries: probability, standard distributions and densities, Bayes rule;

- modelling and learning: models, noise, maximum likelihood learning and error functions; generalisation;

- classification: decision boundaries; k-nn classifier, linear discriminants;

- receiver operating characteristics: loss functions; ROC curves and their optimisation;

- optimisation for learning;

- neural networks, multi-layer feed-forward networks, RBF, back-propagation learning algorithm, auto-encoders;

- kernel-based classifiers, kernel trick;

- feature extraction: sequential forwards/backwards selection;

- unsupervised methods: clustering,  k-means, kernel k-means, cluster validation;

- dimension reduction and visualisation: PCA, ICA, linear and nonlinear methods for visualisation, MDS and isomap.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 42.00 Guided Independent Study 108.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 22 Lectures
Scheduled learning and teaching activities 20 Workshops/tutorials
Guided independent study 50 Individual assessed work
Guided independent study 58 Private study

 

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

Two workshops will be assessed. Other workshops will have formative components.

MSc students will also develop a practical project with formative feedback available. Students on the level-3 version of this module (ECM3420) are not requested to develop a project. Accordingly, for students on ECM3420, the two assessed courseworks will be worth 20% each (40% of the total mark).

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 20 Written Exams 60 Practical Exams 20
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Written exam – closed book 60 2 hours - Summer Exam Period All except 5 Oral on request
Coursework 1 10 25 hours All Written
Coursework 2 10 25 hours All Written
Project 20 50 hours All Oral on request

 

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
Summative Exam Written exam (60%) All except 5 August Ref/Def period
Summative Coursework Coursework (40%) All Completed over summer with a deadline in August

 

RE-ASSESSMENT NOTES

Referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 50% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

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 Duda and Hart Pattern Classification and Scene Analysis 2nd Wiley 2002 0471056693 [Library]
Set Webb, A. Statistical Pattern Recognition 2 Wiley 2002 0-470-84513-9 [Library]
Set Murphy, K. Machine Learning: A Probabilistic Perspective 1st MIT Press 2012 978-0-262-018029 [Library]
Set John C. Bishop Pattern recognition and machine learning Springer 2006 [Library]
Set Haykin, S. Neural Networks and Learning Machines 3 Pearson, Prentice Hall 978-0-13-14713-9- [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Monday 23 April 2018 LAST REVISION DATE Friday 09 August 2019
KEY WORDS SEARCH Data; machine learning; pattern recognition; probability.