Skip to main content

Study information

Machine Learning (Professional) - 2024 entry

MODULE TITLEMachine Learning (Professional) CREDIT VALUE15
MODULE CODEECMM458 MODULE CONVENERDr Tinkle Chugh (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11 0 0
Number of Students Taking Module (anticipated) 90
DESCRIPTION - summary of the module content
*** This module is a “professional” version of the similar module ECMM422. It is intended to be taught in a short-fat format based around 3-day teaching blocks, as part of the MSc Data Science (Professional) programme. ***
 
Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bioinformatics and exploratory data analysis in natural science and engineering. This module will provide you with a thorough grounding in the theory and application of machine learning, pattern recognition, classification, categorisation, and concept acquisition.
 
Pre-Requisite module: ECMM431
 
Co-requisite modules: None.
AIMS - intentions of the module

In this data-driven era, modern technologies are generating massive and high-dimensional datasets. This module aims to give you an understanding of computational methods used in modern data analysis. In particular, this module aims to impart knowledge and understanding of machine learning methods from basic pattern-analysis methods to state-of-the-art research topics; to give you experience of data-modelling development in practical workshops. Neural Networks, Bayesian methods and kernel-based algorithms will be introduced for extracting knowledge from large data sets of patterns (data mining techniques) where it is important to have explicit rules governing machine learning and pattern recognition. Recent development of techniques and algorithms for big-data analysis will also be addressed.

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 advanced and complex principles for statistical machine learning to various data analysis;
 
2. analyse novel pattern recognition and classification problems; establish statistical models for them and write software to solve them;
 
3. apply a range of supervised and unsupervised machine learning techniques to a wide range of real-life applications.
 
Discipline Specific Skills and Knowledge:
 
4. state the importance and difficulty of establishing a principled probabilistic model for pattern recognition;
 
5. apply a number of complex and advanced mathematical and numerical 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;
 
7. critically read and report on research papers;
 
8. conduct small individual research projects.
SYLLABUS PLAN - summary of the structure and academic content of the module
Topics will include:
 
- Introductory material: Practical motivation for machine learning, basic ideas of supervised and unsupervised learning, classification, regression.
 
- Describing data.
 
- Latent descriptions: k-means, maximum likelihood; mixture models; PCA; ICA.
 
- Unsupervised learning: Clustering.
 
- Supervised models: k-nearest neighbours, linear and non-linear regression, linear discriminant analysis, logistic regression, SVM (Support Vector Machines) and maximum margin classifiers.
 
- Loss functions and maximum likelihood estimators.
 
- Neural networks and deep learning.
 
- Evaluation of performance, dataset balance.
 
- Ensemble methods: boosting, bagging, decision trees and random forests Metric learning.
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 30 Guided Independent Study 40 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 20 Lectures
Scheduled learning and teaching activities 10 Workshops/practicals
Guided independent study 20 Coursework preparation
Guided independent study 20 Wider reading and self study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
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 (1 piece)

100

2000-3500 words per piece

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

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
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 Shawe-Taylor, J. and Cristianini, N. Kernel methods for pattern analysis Cambridge University Press 2006 521813972
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 Hastie T., Tibshirani R. & Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd Springer 2009 978-0387848587
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 Thursday 14th March 2024 LAST REVISION DATE Thursday 14th March 2024
KEY WORDS SEARCH Machine learning, statistical modelling

Please note that all modules are subject to change, please get in touch if you have any questions about this module.