Study information

Data-Centric Engineering - 2024 entry

MODULE TITLEData-Centric Engineering CREDIT VALUE15
MODULE CODEENGM010 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 0 11 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)

Programmes that are accredited by the Engineering Council are required to meet Accreditation of Higher Education Programmes (AHEP4) Learning Outcomes.  The Engineering Council AHEP4 Learning Outcomes are taught and assessed on this module and identified in brackets below. 

 

On successful completion of this module you should be able to: 

Module specific skill and knowledge 

 

  1. 1.Understand probabilistic logic and modelling and their relevance to real-world engineering problems. (M1)   

  1. 2.Comprehend statistical inference and its relevance to data-driven engineering.  (M1) 

  1. 3.Formulate probabilistic models to analyse data with applications in engineering.  (M2) 

  1. 4.Apply diagnostic tools to check validity of models. (M2)  

  1. 5.Apply scientific computing skills to handle data and analyse probabilistic models. (M3)   

 

Discipline specific skill and knowledge 

  1. 6.Explain the latest trends in data-driven engineering. (M4) 

 

Personal and key transferable skill and knowledge 

  1. 7.Demonstrate effective teamworking.  (M16) 

  1. 8.Demonstrate improved written and oral communication skills and effective use of learning resources. (M17) 

SYLLABUS PLAN - summary of the structure and academic content of the module
  • Fundamentals of Data Centric Engineering  
  • Probabilistic inference  
  • Maximum likelihood  
  • Revisiting Bayesian modelling  
  • Approximation and computational 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 33 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 22 Lectures
Scheduled learning and teaching activities 11 Tutorials
Guided independent study 117 Reading references; 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 (M1-M3)   
Coursework –team presentation 10 20 mins per presentation 6,7,8 (M4, M16 and M17)  Oral
Coursework – individual project 20 3000 word technical report 4-8 (M2,M3,M4, M16 and M17)  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 (M1,M2, M3) August Ref/Def period

 

RE-ASSESSMENT NOTES

Deferrals: Reassessment will be by coursework and/or exam in the deferred element only. For deferred candidates, the module mark will be uncapped. 

Referrals: Reassessment will be by a single written exam worth 100% of the module. As it is a referral, 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: 

[1] D.J. MacKay, Information Theory, Inference and Learning Algorithms, Cambridge University Press 2003 

[2] D. Calvetti, E. Somersalo, An introduction to Bayesian scientific computing: Ten lectures on subjective computing. Springer 2007 

[3] S. Rogers, M. Girolami, A first course in machine learning, second edition, 2nd Edition, Chapman & Hall/CRC, 

2016 

[4] C. E. Rasmussen, C. K. I. Williams, Gaussian processes for machine learning, Vol. 1, MIT press Cambridge, 

 

Reading list for this module:

There are currently no reading list entries found for this module.

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 Tuesday 14th November 2023 LAST REVISION DATE Friday 18th October 2024
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.