CME8416 : Big Data and AI for Sustainable Engineering
CME8416 : Big Data and AI for Sustainable Engineering
- Offered for Year: 2026/27
- Module Leader(s): Dr Adrian Oila
- Lecturer: Dr Jie Zhang
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
N/A
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
Aims
The aim of this module is to introduce the practical aspects of the basic big data and artificial intelligence (AI) methods used in sustainable engineering processes.
Outline Of Syllabus
-High Performance Computing for big data generation and analysis
-Big data processing tools for sorting, organizing and visualization
-Multivariate statistical data analysis: linear and nonlinear regression
-Machine learning techniques
-Applications of big data and AI techniques for sustainable engineering
Learning Outcomes
Intended Knowledge Outcomes
On completing this module, students will be able to demonstrate knowledge and understanding of:
-Big data generation, processing, analysis and visualization (AHEP4 M1-3)
-Data driven modelling and design assessment of sustainable industrial processes (AHEP4 M2-4)
-AI and machine learning techniques for modelling sustainable industrial processes (AHEP4 M2-4, M7)
Intended Skill Outcomes
On completion of the module students will be able to demonstrate skills in:
-Setup and run atomistic simulations for big data generation (AHEP4 M2-3)
-Creating scripts for sorting, organizing and visualization of big data (AHEP4 M2-3)
-Building, analyzing and evaluating data-driven models using MATLAB (AHEP4 M2-4)
-Analysing data from sustainable industrial processes (AHEP4 M2-4)
-Optimizing industrial processes using surrogate models for enhancing sustainability (AHEP4 M2-4)
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 66:00 | 66:00 | Assessment Preparation and completion |
| Scheduled Learning And Teaching Activities | Lecture | 8 | 2:00 | 16:00 | Lectures |
| Structured Guided Learning | Lecture materials | 10 | 1:00 | 10:00 | Online Materials |
| Scheduled Learning And Teaching Activities | Small group teaching | 8 | 1:00 | 8:00 | Tutorials and formative exercises |
| Guided Independent Study | Independent study | 1 | 100:00 | 100:00 | Review lecture notes, course materials and recommended reading |
| Total | 200:00 |
Teaching Rationale And Relationship
Online materials will be used to introduce the main topics. Scheduled lectures will be used to deliver material not covered in the recorded lectures and also to revise the content of the online materials.
The tutorial sessions are supervised activities in which the students apply the knowledge that they gain during lectures in order to effectively work with big data using the techniques and algorithms presented during lectures.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 2 | M | 50 | Report 1 – big data (approx. 1500-2000 words) - Individual Report |
| Report | 2 | M | 50 | Report 2 – AI (approx. 1500-2000 words) - Individual Report |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Lab exercise | 2 | M | Formative exercise during the tutorial sessions - Individual Work |
Assessment Rationale And Relationship
The two summative reports combined with the formative lab exercises provide an appropriate way to assess both theoretical understanding (AHEP4 M1) and problem solving skills (AHEP4 M2) and software skills (AHEP4 M3). They also develop the ability to select and critically evaluate technical literature (M4), process sustainability and communication skills (AHEP4 M17).
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CME8416's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- CME8416's past Exam Papers
General Notes
N/A
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Disclaimer
The information contained within the Module Catalogue relates to the 2026 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, staffing changes, and student feedback. Module information for the 2027/28 entry will be published here in early-April 2027. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.