Module Information | University of Exeter

MODULE TITLE	Machine Learning (Professional)	CREDIT VALUE	15
MODULE CODE	ECMM458	MODULE CONVENER	Dr 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

Machine Learning (Professional) - 2024 entry