Study information

Data-Centric Engineering - 2023 entry

MODULE TITLEData-Centric Engineering CREDIT VALUE15
MODULE CODEENGM010 MODULE CONVENERDr Hussein Rappel (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 12 0
Number of Students Taking Module (anticipated)
DESCRIPTION - summary of the module content

The next decade will see a step changes in data-driven technology, impacting all aspects of engineering and industry. By exploiting data being generated presents enormous engineering opportunities to transform both system design and control.
 
This module focuses on the logic, algorithms, and frameworks that are essential to tackle real-world data and the grand challenges of modern data-driven engineering applicable to the domains such as materials, patient-specific medicine, virtual prototyping, and sustainability.
 
The module will introduce the students to mathematical foundations and state-of-the-art methods in probabilistic modelling, Bayesian analysis, and probabilistic machine learning.
 
AIMS - intentions of the module

The module aims at providing a course in mathematical foundations and advanced methods for data-centric engineering at the frontiers of the research of interest at the University of Exeter.

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:

 1. Understand probabilistic logic and modelling and their relevance to real-world engineering problems

 2. Comprehend statistical inference and its relevance to data-driven engineering

 3. Formulate probabilistic models to analyse data with applications in engineering

 4. Apply diagnostic tools to check validity of models

 5. Apply scientific computing (python) skills to handle data and analyse probabilistic models

 6. Explain the latest trends in data-driven engineering

 7. Demonstrate improved written and oral communication skills

 8. Effective use of learning resources

 

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

- Basics

- Probability and axioms, probability density, distribution

- Sum, product rule, and Bayes' rule

- Expectation, mean, variance, median

- Frequentist vs Bayesian

- Curve fitting and regression

- Least-squares methods

- Maximum likelihood

- Fundamental problems

- Single-parameter model

- Multi-parameter models

- Revisiting regression

- Bayesian regression

- Model checking

- Bayesian model comparison

- Approximation and computational topics

- Laplace's method

- Sampling1: preliminaries

- Sampling2: advanced

- Markov chain simulation

- Advanced topics

- Introduction to Gaussian processes and their applications
 

 
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 36 Guided Independent Study 117 Placement / Study Abroad
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 24 Lectures
Scheduled learning and teaching activities 12 Tutorials
Guided independent study 117 Reading lecture notes; working exercises

 

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

N/A

SUMMATIVE ASSESSMENT (% of credit)
Coursework 30 Written Exams 70 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 70 2 hours 1-4  
Coursework –team presentation 10 20 mins per presentation 6,7,8 Oral
Coursework – individual project 20 3000 word technical report 4-8 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
All above Written exam (100%) 1-4 August Ref/Def period

 

RE-ASSESSMENT NOTES

Reassessment will be by a single written exam only worth 100% of the module. For deferred candidates, the mark will be uncapped. For referred candidates, the mark 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 Bishop, C. Pattern Recognition and Machine Learning 1 Springer 2006 978-0387310732
Set Brémaud, Pierre An introduction to probabilistic modeling Springer 1988
Set Calvetti, D. and E. Somersalo An introduction to Bayesian scientific computing: Ten lectures on subjective computing Springer 2007
Set Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. and Rubin, D.B. Bayesian Data Analysis CRC Press 2013
Set Mackay, D. J. Information Theory, Inference and Learning Algorithms Cambridge University Press 2003
Set Rasmussen, C.E. and Williams C.K.I. Gaussian Processes for Machine Learning Cambridge, MA: MIT Press. 2006 0-262-18253-X
Set Rogers, S. and M. Girolami A first course in machine learning 2nd Chapman & Hall/CRC 2016
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 Friday 27th January 2023 LAST REVISION DATE Friday 2nd June 2023
KEY WORDS SEARCH None Defined

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