Computer Science

ECM3420 - Learning from Data (2019)

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MODULE TITLELearning from Data CREDIT VALUE15
MODULE CODEECM3420 MODULE CONVENERDr Lorenzo Livi (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 12 weeks 0 0
Number of Students Taking Module (anticipated) 60
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.

 

Prerequisite module: ECM1400, ECM1415 or ECM1701

 

AIMS - intentions of the module

This module aims to equip you with the fundamentals of machine learning in a computer science context. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods.  We will pay particular attention to methods 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 apply principles for statistical pattern recognition to novel data;
2 analyse novel pattern recognition and classification problems, establish models for them and write software to solve them;
3 utilise a range of supervised and unsupervised pattern recognition and machine learning techniques to solve a wide range of problems.
 

Discipline Specific Skills and Knowledge:

4 state the importance and difficulty of establishing principled models for pattern recognition;
5 use Matlab or other programming languages for scientific analysis and simulation.
 

Personal and Key Transferable / Employment Skills and Knowledge:

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; common neural network architectures;

- optimisation for learning;

- classification: decision boundaries; k-nn classifier, linear discriminants, kernel-based classifiers, kernel trick, large margin classifiers;

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

- unsupervised methods: clustering, k-means;

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

- feature extraction: sequential forwards/backwards selection;

- learning systems with temporal coupling: hidden Markov models; object tracking in video.

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.

SUMMATIVE ASSESSMENT (% of credit)
Coursework 40 Written Exams 60 Practical Exams 0
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 20 25 hours All Written
Coursework 2 20 25 hours 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-reassessment
All above Written exam (60%) All except 5 August Ref/Def period
All above 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 40% 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


 

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 Christopher Bishop 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 Kevin Murphy Machine Learning: A Probabilistic Perspective 1st MIT Press 2012 978-0-262-018029 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECM1701, ECM1415, ECM1400
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 3 (NQF level 6) AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Thursday 06 July 2017 LAST REVISION DATE Tuesday 23 July 2019
KEY WORDS SEARCH Data; machine learning; pattern recognition; probability.