EEE8161 : Machine Learning for Engineering Applications
- Offered for Year: 2025/26
- Module Leader(s): Dr Kabita Adhikari
- Lecturer: Dr Jingjing 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
This module aims to provide underlying mathematical, statistical and theoretical concepts of Machine Learning along with essential programming skills and expertise to design, build, and implement appropriate Machine Learning techniques for various engineering applications. The module introduces classical regression, classification, clustering and modern deep learning models. The module includes relevant programming exercises to complement the theoretical concepts, which will allow students to gain valuable conceptual and programming skills to build, optimise and implement these models into a range of practical engineering challenges.
Outline Of Syllabus
This module introduces mathematical foundations and fundamental concepts of machine learning, including supervised, unsupervised, clustering, and modern deep learning methods. The module focuses on mathematically formulating, and practically designing, testing, and validating different types of machine learning algorithms to address various real-world applications. The course delves into data processing and compression techniques, aiding in the interpretation of crucial information extracted by diverse machine learning models. Moreover, the module covers contemporary decision trees, random forests and deep learning algorithms, exploring concepts such as neurons and brains, artificial neural networks, multilayer perceptron, convolutional neural networks, recurrent neural networks, and reinforcement learning. Additionally, it provides an introduction to emerging machine learning paradigms like Tsetlin machines. Ethical considerations, as well as issues regarding privacy and security related to data-driven machine learning models, are also addressed in this module.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 2:00 | 2:00 | NUMBAS-based Exam |
| Scheduled Learning And Teaching Activities | Lecture | 12 | 2:00 | 24:00 | In person lectures |
| Structured Guided Learning | Lecture materials | 30 | 0:20 | 10:00 | Pre-recorded video lectures. All the lectures are delivered in person; these videos are just additional materials if any student wishes to go through the materials before the scheduled lectures. |
| Guided Independent Study | Assessment preparation and completion | 20 | 2:00 | 40:00 | Preparation and completion of summative coursework that includes programming assignment |
| Guided Independent Study | Assessment preparation and completion | 18 | 1:00 | 18:00 | Completion of take-home lab exercises and quizzes |
| Scheduled Learning And Teaching Activities | Practical | 16 | 2:00 | 32:00 | Lab sessions focused on programming exercises to build and test machine learning models |
| Guided Independent Study | Independent study | 54 | 1:00 | 54:00 | Lectures follow up: Reviewing lecture materials, building understanding and creating comments on provided lecture documents |
| Guided Independent Study | Independent study | 20 | 1:00 | 20:00 | Revision for final exam |
| Total | 200:00 |
Teaching Rationale And Relationship
The module employs blended teaching methods, including recorded lecture videos, scheduled in-person lectures, and Q&A/discussion sessions, which encompass a diverse array of teaching approaches for delivering the theoretical and conceptual foundations of machine learning. Additionally, problem-based programming lab sessions provide opportunities to construct, test, and implement the machine learning concepts and theories covered in the lectures.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Digital Examination | 120 | 2 | A | 100 | Online NUMBAS-based Exam |
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 | Machine Learning Programming exercise- MATLAB Warmup exercise. |
| Lab exercise | 2 | M | Machine Learning Programming exercise - Linear Regression. |
| Lab exercise | 2 | M | Machine Learning Programming exercise - Multivariate Linear Regression. |
| Lab exercise | 2 | M | Machine Learning Programming exercise - Logistic Regression. |
| Lab exercise | 2 | M | Machine Learning Programming exercise - Principal Component Analysis. |
| Lab exercise | 2 | M | Machine Learning Programming exercise - K-Means Clustering. |
| Prob solv exercises | 2 | M | Quiz - Introduction to Machine Learning |
| Prob solv exercises | 2 | M | Quiz - Linear Regression |
| Prob solv exercises | 2 | M | Quiz - Multivariate Linear Regression |
| Prob solv exercises | 2 | M | Quiz - Logistic Regression |
| Prob solv exercises | 2 | M | Quiz - PCA |
| Prob solv exercises | 2 | M | Quiz - K-Means Clustering |
| Prob solv exercises | 2 | M | Quiz - Evaluation Metrics of ML models |
| Lab exercise | 2 | M | Fundamentals of Python Programming |
| Lab exercise | 2 | M | Unsupervised Learning |
| Lab exercise | 2 | M | Simple Supervised Learning |
| Lab exercise | 2 | M | Neural Networks |
| Lab exercise | 2 | M | Lab Challenge 1 – Simple Supervised Learning |
| Lab exercise | 2 | M | Lab Challenge 2 – Neural Networks |
| Prob solv exercises | 2 | M | Quiz 1 – Machine Learning Fundamentals |
| Prob solv exercises | 2 | M | Quiz 2 - Machine Learning Models |
Assessment Rationale And Relationship
The final NUMBAS-based exam will evaluate students' knowledge and understanding on the fundamental principles and applications of machine Learning techniques. Additionally, the exam will assess students' abilities to formulate, design, and select various machine learning models, as well as their capacity to interpret key information derived from these models. Furthermore, it will test students' capability in evaluating ethical and societal issues associated with data-driven machine learning models.
The formative machine learning programming exercises will reinforce understanding of the machine learning methods and their practical implementation/testing. The formative online quizzes will provide feedback to students on their grasp of these topics and readiness for the exam.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- EEE8161's Timetable