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EEE8161 : Machine Learning for Engineering Applications

  • Offered for Year: 2024/25
  • Module Leader(s): Dr Kabita Adhikari
  • Lecturer: Dr Rishad Shafik, Dr Jingjing Zhang
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus

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


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 StudyAssessment preparation and completion12:002:00NUMBAS-based Exam
Scheduled Learning And Teaching ActivitiesLecture122:0024:00In person lectures
Structured Guided LearningLecture materials300:2010:00Pre-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 StudyAssessment preparation and completion202:0040:00Preparation and completion of summative coursework that includes programming assignment
Guided Independent StudyAssessment preparation and completion181:0018:00Completion of take-home lab exercises and quizzes
Scheduled Learning And Teaching ActivitiesPractical162:0032:00Lab sessions focused on programming exercises to build and test machine learning models
Guided Independent StudyIndependent study201:0020:00Revision for final exam
Guided Independent StudyIndependent study541:0054:00Lectures follow up: Reviewing lecture materials, building understanding and creating comments on provided lecture documents
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

Description Length Semester When Set Percentage Comment
Digital Examination1202A100Online 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
Written exercise2MMachine Learning Programming exercises. Students will complete the set programming exercises to design and verify Machine learning models
Written exercise2MOnline Quizzes. Students will complete online quizzes to check their understanding of the taught materials.
Written exercise2MFormative – in preparation of the final NUMBAS test
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