Study information

# Data-Centric Engineering - 2023 entry

MODULE TITLE CREDIT VALUE Data-Centric Engineering 15 ENGM010 Dr Hussein Rappel (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 12 0
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

- Markov chain simulation

- 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 Guided Independent Study 36 117
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 Written Exams Practical Exams 30 70 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

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 ECTS VALUE 15 7.5
PRE-REQUISITE MODULES None None
NQF LEVEL (FHEQ) AVAILABLE AS DISTANCE LEARNING 7 No Friday 27th January 2023 Friday 2nd June 2023
KEY WORDS SEARCH None Defined