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 | |
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
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 8 | 2:00 | 16:00 | Lectures |
| Structured Guided Learning | Lecture materials | 10 | 1:00 | 10:00 | Online Materials |
| Guided Independent Study | Assessment preparation and completion | 1 | 66:00 | 66:00 | Assessment Preparation and completion |
| 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.
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).
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CME8416's Timetable