EGB150 Machine Learning for Engineering I
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | EGB150 |
|---|---|
| Prerequisite(s): | EGB103 |
| Antirequisite(s): | CAB320 or IFB320 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
Unit Outline: Semester 1 2027, Gardens Point, Internal
| Unit code: | EGB150 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | EGB103 |
| Anti-requisite: | CAB320, IFB320 |
Overview
Machine learning is the science of getting computers to act without being explicitly programmed. This is a foundational machine learning unit that, via a series of engineering case studies, explores fundamental machine learning concepts and methods and how they can be applied to a variety of engineering contexts. Topics include: definition of machine learning tasks, and types of machine learning problems; an introduction to the machine learning development cycle, including data collection and curation, model training, and evaluation; introduction to classification, regression, clustering and dimension reduction methods. The unit makes use of python, jupyterlab, git and state of the art machine learning libraries. Case studies and applications considered in the unit will draw from a variety of engineering disciplines including electrical, civil and mechanical engineering.
Learning Outcomes
On successful completion of this unit you will be able to:
- Describe common ML problem types and baseline methods (classification, regression, clustering) and select appropriate approaches for engineering problems.
- Implement and evaluate ML models in Python (with proper splitting, metrics, and visualisation) and interpret performance.
- Manage small ML tasks end‑to‑end (data handling, training, validation) and document reproducible workflows.
- Communicate model results and limitations effectively to expert and non‑expert audiences.
- Identify ethical risks (data bias, misuse) and make responsible choices in the application of ML to engineering problems.
Content
Via a series of engineering applications and case studies, the following topics will be covered:
- Introduction to machine learning, including key problem types (classification, regression, clustering), and families of machine learning methods (supervised, unsupervised, reinforcement)
- Data and machine learning, including how data is split and organised for model development, and how data availability can inform modelling choices
- Linear and regularized regression
- Classification methods, including k-nearest neighbours classification, decision trees and random forests, and support vector machines
- Clustering methods, including K-means, GMMs, HAC and DBScan
- Dimension reduction methods including PCA and LDA
- Introduction to neural networks
- Visualisation methods to both explore model performance, and explore the underlying data (tSNE and UMAP)
Learning Approaches
This unit will use mixed-mode delivery. Content introducing machine learning concepts will be delivered via pre-class videos and support weekly interactive lecture consultations that will consider engineering case studies and demonstrate how machine learning methods can be applied to address diverse engineering applications. Practical coding labs will allow you to apply machine learning concepts hands-on using Python and modern machine learning libraries.
In this unit, you can expect the following timetabled activities:
- Weekly interactive lecture consultations, which focus on applying machine learning methods to real‑world engineering problems, and evaluating their performance and suitability.
- Weekly coding practical labs, applying introduced machine learning methods and concepts to engineering problems through coding exercises in JupyterLab.
To complement timetabled activities, you will be provided with:
- Pre-recorded video content addressing fundamental machine learning concepts and methods.
- Video demonstrations of problem solutions and ML workflows.
- Sample datasets and notebooks for independent practice.
You are expected to:
- Review pre-recorded video content: Review weekly content prior to timetabled activities each week to build theoretical understanding and enable effective participation in class activities.
- Complete weekly coding tasks and problem-solving exercises: Attempt these independently before the practical sessions, supported by provided resources and video walkthroughs, to reinforce theoretical understanding.
- Engage in in-class challenges: Participate actively in collaborative coding and problem-solving during classes.
Feedback on Learning and Assessment
You will receive formative oral feedback on your progress in this unit during practicals and discussions throughout the semester. You will receive written feedback on the unit's problem-solving tasks as well as the tutorial and Python work. Formative practice and feedback on exam related concepts will also be covered in lectures and practical. Additional feedback on a group or individual basis will also be available through appointment with a member of the teaching team.
Assessment
Overview
Assessment in the unit consists of a series of authentic problem-solving tasks throughout the semester of increasing complexity involving machine learning in engineering systems and related components. This is reinforced with Python exercises to relate theory to practice and a final exam covering all major concepts and learning across the unit.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Assignment 1
You are a transport engineer tasked with modelling traffic times by applying the theoretical concepts and presenting your results in a report. The assignment includes a solution report (25%). The design and analysis will rely on the use of Python.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Assessment: Assignment 2
You are a medical engineer evaluating patient outcomes for a medical trial and presenting your results in a report (25%). The design and analysis will rely on the use of Python.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Assessment: Examination
This tests all of the major concepts covered in the unit including principles, problem-solving, and analysis of machine learning in engineering systems.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
You will be assisted with lecture notes, and other written and or video recorded resource materials. You will also be referred to several reference books in machine learning. The unit's Canvas site will provide access to various resource materials.
Resource Materials
Reference book(s)
Title: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3ed: Concepts, Tools, and Techniques to Build Intelligent Systems
Author: Aurélien Géron
Publisher: O'Reilly Media
Year: 2022
Title: Pattern Recognition and Machine Learning
Author: Christopher M. Bishop.
Publisher: Springer
Year: 2016
Risk Assessment Statement
You will undertake weekly lecture consultations and tutorials in the traditional classrooms and lecture theatres. As such, there are no extraordinary workplace health and safety issues associated with these components of the unit.
You will be required to undertake practical sessions in the computer laboratory under the supervision of the teaching team members. You will undergo a health and safety induction before the commencement of the practical sessions, both an online induction and an in-laboratory safety induction. If you do not complete both safety inductions, you will be denied access to laboratories.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EN01 Bachelor of Engineering (Honours)
- Make decisions ethically within the social, cultural, and organisational contexts of professional engineering practice.
Relates to: ULO5, Assignment 2, Examination - Engage stakeholders professionally and communicate the outcomes of your work effectively to expert and non-expert audiences using appropriate modes.
Relates to: ULO4, Assignment 2 - Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: Assignment 1, Assignment 2 - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: ULO1, ULO2, ULO3, Assignment 1, Assignment 2, Examination - Deploy appropriate approaches to engineering design and quality.
Relates to: ULO2, Assignment 1, Assignment 2, Examination - Engage with and apply regulatory requirements relating to safety, risk management, and sustainability in professional engineering practice.
Relates to: ULO5, Assignment 2, Examination - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: ULO1, ULO2, Assignment 1, Examination
Unit Outline: Semester 2 2027, Gardens Point, Internal
| Unit code: | EGB150 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | EGB103 |
| Anti-requisite: | CAB320, IFB320 |
Overview
Machine learning is the science of getting computers to act without being explicitly programmed. This is a foundational machine learning unit that, via a series of engineering case studies, explores fundamental machine learning concepts and methods and how they can be applied to a variety of engineering contexts. Topics include: definition of machine learning tasks, and types of machine learning problems; an introduction to the machine learning development cycle, including data collection and curation, model training, and evaluation; introduction to classification, regression, clustering and dimension reduction methods. The unit makes use of python, jupyterlab, git and state of the art machine learning libraries. Case studies and applications considered in the unit will draw from a variety of engineering disciplines including electrical, civil and mechanical engineering.
Learning Outcomes
On successful completion of this unit you will be able to:
- Describe common ML problem types and baseline methods (classification, regression, clustering) and select appropriate approaches for engineering problems.
- Implement and evaluate ML models in Python (with proper splitting, metrics, and visualisation) and interpret performance.
- Manage small ML tasks end‑to‑end (data handling, training, validation) and document reproducible workflows.
- Communicate model results and limitations effectively to expert and non‑expert audiences.
- Identify ethical risks (data bias, misuse) and make responsible choices in the application of ML to engineering problems.
Content
Via a series of engineering applications and case studies, the following topics will be covered:
- Introduction to machine learning, including key problem types (classification, regression, clustering), and families of machine learning methods (supervised, unsupervised, reinforcement)
- Data and machine learning, including how data is split and organised for model development, and how data availability can inform modelling choices
- Linear and regularized regression
- Classification methods, including k-nearest neighbours classification, decision trees and random forests, and support vector machines
- Clustering methods, including K-means, GMMs, HAC and DBScan
- Dimension reduction methods including PCA and LDA
- Introduction to neural networks
- Visualisation methods to both explore model performance, and explore the underlying data (tSNE and UMAP)
Learning Approaches
This unit will use mixed-mode delivery. Content introducing machine learning concepts will be delivered via pre-class videos and support weekly interactive lecture consultations that will consider engineering case studies and demonstrate how machine learning methods can be applied to address diverse engineering applications. Practical coding labs will allow you to apply machine learning concepts hands-on using Python and modern machine learning libraries.
In this unit, you can expect the following timetabled activities:
- Weekly interactive lecture consultations, which focus on applying machine learning methods to real‑world engineering problems, and evaluating their performance and suitability.
- Weekly coding practical labs, applying introduced machine learning methods and concepts to engineering problems through coding exercises in JupyterLab.
To complement timetabled activities, you will be provided with:
- Pre-recorded video content addressing fundamental machine learning concepts and methods.
- Video demonstrations of problem solutions and ML workflows.
- Sample datasets and notebooks for independent practice.
You are expected to:
- Review pre-recorded video content: Review weekly content prior to timetabled activities each week to build theoretical understanding and enable effective participation in class activities.
- Complete weekly coding tasks and problem-solving exercises: Attempt these independently before the practical sessions, supported by provided resources and video walkthroughs, to reinforce theoretical understanding.
- Engage in in-class challenges: Participate actively in collaborative coding and problem-solving during classes.
Feedback on Learning and Assessment
You will receive formative oral feedback on your progress in this unit during practicals and discussions throughout the semester. You will receive written feedback on the unit's problem-solving tasks as well as the tutorial and Python work. Formative practice and feedback on exam related concepts will also be covered in lectures and practical. Additional feedback on a group or individual basis will also be available through appointment with a member of the teaching team.
Assessment
Overview
Assessment in the unit consists of a series of authentic problem-solving tasks throughout the semester of increasing complexity involving machine learning in engineering systems and related components. This is reinforced with Python exercises to relate theory to practice and a final exam covering all major concepts and learning across the unit.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Assignment 1
You are a transport engineer tasked with modelling traffic times by applying the theoretical concepts and presenting your results in a report. The assignment includes a solution report (25%). The design and analysis will rely on the use of Python.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Assessment: Assignment 2
You are a medical engineer evaluating patient outcomes for a medical trial and presenting your results in a report (25%). The design and analysis will rely on the use of Python.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Assessment: Examination
This tests all of the major concepts covered in the unit including principles, problem-solving, and analysis of machine learning in engineering systems.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
You will be assisted with lecture notes, and other written and or video recorded resource materials. You will also be referred to several reference books in machine learning. The unit's Canvas site will provide access to various resource materials.
Resource Materials
Reference book(s)
Title: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3ed: Concepts, Tools, and Techniques to Build Intelligent Systems
Author: Aurélien Géron
Publisher: O'Reilly Media
Year: 2022
Title: Pattern Recognition and Machine Learning
Author: Christopher M. Bishop.
Publisher: Springer
Year: 2016
Risk Assessment Statement
You will undertake weekly lecture consultations and tutorials in the traditional classrooms and lecture theatres. As such, there are no extraordinary workplace health and safety issues associated with these components of the unit.
You will be required to undertake practical sessions in the computer laboratory under the supervision of the teaching team members. You will undergo a health and safety induction before the commencement of the practical sessions, both an online induction and an in-laboratory safety induction. If you do not complete both safety inductions, you will be denied access to laboratories.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EN01 Bachelor of Engineering (Honours)
- Make decisions ethically within the social, cultural, and organisational contexts of professional engineering practice.
Relates to: ULO5, Assignment 2, Examination - Engage stakeholders professionally and communicate the outcomes of your work effectively to expert and non-expert audiences using appropriate modes.
Relates to: ULO4, Assignment 2 - Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: Assignment 1, Assignment 2 - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: ULO1, ULO2, ULO3, Assignment 1, Assignment 2, Examination - Deploy appropriate approaches to engineering design and quality.
Relates to: ULO2, Assignment 1, Assignment 2, Examination - Engage with and apply regulatory requirements relating to safety, risk management, and sustainability in professional engineering practice.
Relates to: ULO5, Assignment 2, Examination - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: ULO1, ULO2, Assignment 1, Examination