CAB420 Machine Learning
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: | CAB420 |
|---|---|
| Prerequisite(s): | (CAB201 or CAB202 or ITD121 or IFN501 or IFN556 or (Admission to EN50 or EN55 or EN52 or EN56 or EN62 or EN72)) or (192cps in SV03 or IV04 or MV05 or EV08) or (Admission to IV53 or IV54 or IV55 or IV56 or IV58). |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,037 |
| Domestic tuition unit fee | $3,828 |
| International unit fee | $4,560 |
Unit Outline: Semester 1 2023, Gardens Point, Internal
| Unit code: | CAB420 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | CAB201 OR CAB202 OR ITD121 OR IFN501 OR IFN556 OR Admission to (EN50 or EN55 or EN52) |
| Coordinator: | Simon Denman | s.denman@qut.edu.au |
Overview
Machine learning is the science of getting computers to act without being explicitly programmed. This unit provides you with a broad introduction to machine learning and its statistical foundations. Topics include: definition of machine learning tasks; classification principles and methods; dimensionality reduction/subspace methods; graphical models; and deep learning. Application examples are taken from areas such as computer vision, finance, market prediction and information retrieval.
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply the principles and concepts of machine learning science using a range of tools and techniques.
- Critically evaluate different machine learning algorithms in a range of complex business, science, engineering, and health contexts
- Reflect on the ethical considerations that arise in applying machine learning in real-world settings
- Research cutting edge developments in machine learning and communicate findings to a specialised audience
- Critically analyse how artificial neural networks relate to the human brain and parallel hardware.
Content
The following topics will be covered:
- Introduction to Machine Learning
- Regression Techniques
- Classification Methods
- Dimensionality Reduction Methods
- Optimization in Machine Learning
- Deep Learning
- Machine Learning Applications including comptuer vision, audio processing, and forecasting
Learning Approaches
This unit is available for you to study in either on-campus or online mode. Learning in this unit includes weekly pre-recorded lectures, online activities, workshops (2 hours per week, in person or online) and a unit communications channel, designed to facilitate communication with your peers and teaching staff outside of scheduled classes. Relevant maths content will be embedded within the lecture sessions. Practical sessions will provide opportunities to explore the underlying theory, practice in the application of theory and algorithms, and allow exploration of concepts with tutors and other students. The assignments require an integrated understanding of the subject matter, and promote required knowledge and skills. You can expect to spend between 10 - 15 hours per week on average involved in preparing for and attending all scheduled workshops, completing assessment tasks, and undertaking your own independent study to consolidate your learning.
Feedback on Learning and Assessment
Feedback in this unit will be provided in the following ways:
- Formative oral feedback will be offered by the lecturer and tutors during the semester to assist you in the development of your skills.
- Formative written feedback through criteria sheet grading.
- In addition to CRA (criteria sheet), comments on summative assessment will be provided.
- Generic comments will be provided to the cohort through the Canvas.
Assessment
Overview
The assessment for this unit is comprised of three programming assignments, a final project, and a final exam (formal written examination).
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
This will consist of 2 small problem-solving tasks that explore the application of machine learning methods. Detailed descriptions will be released on the Canvas website under the 'assessment' section.
This is an assignment for the purposes of an extension.
Assessment: Project (applied)
A project proposal (2 page maximum): The project proposal should include the following information: project title, project idea, brief background, datasets, timeline and the team members.
Presentation: All projects will have a 5 min presentation. At least one project member should be present for the presentation.
Final Report: You must turn in a ~10-page report roughly having the following sections: Introduction; Background; Proposed method; Analysis behind your approach, Details of the experiments and Conclusions.
This is an assignment for the purposes of an extension.
Assessment: Examination (written)
A set of questions on major concepts and problem solving from all the unit material.
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
There is no required text book. Contents from latest publications in top-tier machine learning journals will be used and referenced during the lectures.
Risk Assessment Statement
No particular risk is associated to this unit.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Australian Computer Society Core Body of Knowledge
1: ICT Professional Knowledge
3: Technology Resources
- Hardware and software fundamentals
Relates to: ULO5
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EN52 Master of Robotics and Artificial Intelligence
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices in Robotics and AI
Relates to: ULO4, Problem Solving Task, Project (applied), Examination (written) - Critically analyse, evaluate and apply appropriate methods to Robotics and AI problems to achieve research-informed solutions
Relates to: Problem Solving Task, Project (applied), Examination (written) - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: ULO4, Project (applied) - Work independently and collaboratively demonstrating ethical and socially responsible practice
Relates to: Project (applied)
EV01 Bachelor of Engineering (Honours)
- Make decisions ethically within the social, cultural, and organisational contexts of professional engineering practice.
Relates to: Project (applied) - Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: Project (applied) - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: Problem Solving Task, Project (applied) - Deploy appropriate approaches to engineering design and quality.
Relates to: Examination (written) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Demonstrate a thorough understanding of one engineering discipline, its research directions, and its application in contemporary professional engineering practice.
Relates to: Project (applied)
IN01 Bachelor of Information Technology
- Demonstrate well-developed IT discipline knowledge
Relates to: ULO1 - Employ appropriate IT Methods
Relates to: ULO2 - Work independently and within effective teams
Relates to: ULO4 - Purposefully appraise personal values, attitudes and performance in your continuing professional development
Relates to: ULO3, ULO5
Unit Outline: Semester 1 2023, Online
| Unit code: | CAB420 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | CAB201 OR CAB202 OR ITD121 OR IFN501 OR IFN556 OR Admission to (EN50 or EN55 or EN52) |
Overview
Machine learning is the science of getting computers to act without being explicitly programmed. This unit provides you with a broad introduction to machine learning and its statistical foundations. Topics include: definition of machine learning tasks; classification principles and methods; dimensionality reduction/subspace methods; graphical models; and deep learning. Application examples are taken from areas such as computer vision, finance, market prediction and information retrieval.
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply the principles and concepts of machine learning science using a range of tools and techniques.
- Critically evaluate different machine learning algorithms in a range of complex business, science, engineering, and health contexts
- Reflect on the ethical considerations that arise in applying machine learning in real-world settings
- Research cutting edge developments in machine learning and communicate findings to a specialised audience
- Critically analyse how artificial neural networks relate to the human brain and parallel hardware.
Content
The following topics will be covered:
- Introduction to Machine Learning
- Regression Techniques
- Classification Methods
- Dimensionality Reduction Methods
- Optimization in Machine Learning
- Deep Learning
- Machine Learning Applications including comptuer vision, audio processing, and forecasting
Learning Approaches
This unit is available for you to study in either on-campus or online mode. Learning in this unit includes weekly pre-recorded lectures, online activities, workshops (2 hours per week, in person or online) and a unit communications channel, designed to facilitate communication with your peers and teaching staff outside of scheduled classes. Relevant maths content will be embedded within the lecture sessions. Practical sessions will provide opportunities to explore the underlying theory, practice in the application of theory and algorithms, and allow exploration of concepts with tutors and other students. The assignments require an integrated understanding of the subject matter, and promote required knowledge and skills. You can expect to spend between 10 - 15 hours per week on average involved in preparing for and attending all scheduled workshops, completing assessment tasks, and undertaking your own independent study to consolidate your learning.
Feedback on Learning and Assessment
Feedback in this unit will be provided in the following ways:
- Formative oral feedback will be offered by the lecturer and tutors during the semester to assist you in the development of your skills.
- Formative written feedback through criteria sheet grading.
- In addition to CRA (criteria sheet), comments on summative assessment will be provided.
- Generic comments will be provided to the cohort through the Canvas.
Assessment
Overview
The assessment for this unit is comprised of three programming assignments, a final project, and a final exam (formal written examination).
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
This will consist of 2 small problem-solving tasks that explore the application of machine learning methods. Detailed descriptions will be released on the Canvas website under the 'assessment' section.
This is an assignment for the purposes of an extension.
Assessment: Project (applied)
A project proposal (2 page maximum): The project proposal should include the following information: project title, project idea, brief background, datasets, timeline and the team members.
Presentation: All projects will have a 5 min presentation. At least one project member should be present for the presentation.
Final Report: You must turn in a ~10-page report roughly having the following sections: Introduction; Background; Proposed method; Analysis behind your approach, Details of the experiments and Conclusions.
This is an assignment for the purposes of an extension.
Assessment: Examination (written)
A set of questions on major concepts and problem solving from all the unit material.
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
There is no required text book. Contents from latest publications in top-tier machine learning journals will be used and referenced during the lectures.
Risk Assessment Statement
No particular risk is associated to this unit.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Australian Computer Society Core Body of Knowledge
1: ICT Professional Knowledge
3: Technology Resources
- Hardware and software fundamentals
Relates to: ULO5
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EN52 Master of Robotics and Artificial Intelligence
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices in Robotics and AI
Relates to: ULO4, Problem Solving Task, Project (applied), Examination (written) - Critically analyse, evaluate and apply appropriate methods to Robotics and AI problems to achieve research-informed solutions
Relates to: Problem Solving Task, Project (applied), Examination (written) - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: ULO4, Project (applied) - Work independently and collaboratively demonstrating ethical and socially responsible practice
Relates to: Project (applied)
EV01 Bachelor of Engineering (Honours)
- Make decisions ethically within the social, cultural, and organisational contexts of professional engineering practice.
Relates to: Project (applied) - Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: Project (applied) - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: Problem Solving Task, Project (applied) - Deploy appropriate approaches to engineering design and quality.
Relates to: Examination (written) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Demonstrate a thorough understanding of one engineering discipline, its research directions, and its application in contemporary professional engineering practice.
Relates to: Project (applied)
IN01 Bachelor of Information Technology
- Demonstrate well-developed IT discipline knowledge
Relates to: ULO1 - Employ appropriate IT Methods
Relates to: ULO2 - Work independently and within effective teams
Relates to: ULO4 - Purposefully appraise personal values, attitudes and performance in your continuing professional development
Relates to: ULO3, ULO5