CME8416: Big Data and AI for Sustainable Engineering
- Offered for Year: 2025/26
- Module Leader(s): Dr Adrian Oila
- Owning School: School of Engineering
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
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
The module covers concepts such as:
- 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
- Data driven modelling of sustainable industrial processes
- AI and machine learning techniques for modelling sustainable industrial processes
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
- Creating scripts for sorting, organizing and visualization of big data
- Building, analyzing and evaluating data-driven models using MATLAB
- Analysing data from sustainable industrial processes
- Optimizing industrial processes using surrogate models for enhancing sustainability
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture materials | 10 | 1:00 | 10:00 | Online Materials |
| Scheduled Learning And Teaching Activities | Lecture | 8 | 2:00 | 16:00 | Lectures |
| 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 |
| Guided Independent Study | Assessment preparation and completion | 1 | 66:00 | 66:00 |
Assessment preparation and completion |
| 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.
Assessment Methods
The format of resits will be determined by the Board of Examiners.
Formative Assessment
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Lab exercise 1 | 2 | M | Formative exercise during the tutorial sessions |
Other Assessment
| Component | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report 1 | 2 | M | 50 | Report 1 - big data (approx. 1500-2000 words) |
| Report 2 | 2 | M | 50 | Report 2 - AI (approx. 1500-2000 words) |
Assessment Rationale And Relationship
The two summative reports combined with the formative lab exercises provide an appropriate way to assess both theoretical understanding (M1) and problem-solving skills (M2) and software skills (M3). They also develop the ability to select and critically evaluate technical literature (M4) and communication skills (M17).
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/