General Information
This subject is designed for students who already have a basic understanding of machine learning and want to deepen their knowledge using more advanced techniques. The subject focuses on advanced machine learning methods that are relevant and effective in many real-life and business applications. Students will be provided the necessary tools to wrangle data, implement and train machine learning models, and evaluate the performance and feasibility of these models in the context of the environment where these models are going to be applied. Advanced visualisation tools will be used to create dynamic visual representations of data.
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Details
Academic unit: Bond Business School Subject code: DTSC13-306 Subject title: Advanced Machine Learning Subject level: Undergraduate Semester/Year: September 2023 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Computer Lab: x12 (Total hours: 24) - Computer Lab 1
- Computer Lab: x12 (Total hours: 24) - Computer Lab 2
- Personal Study Hours: x12 (Total hours: 72) - Recommended study time & reviewing materials
Attendance and learning activities: Attendance at all sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. -
Resources
Prescribed resources: No Prescribed resources.
After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.iLearn@Bond & Email: iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications.
Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.
To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
| Academic unit: | Bond Business School |
|---|---|
| Subject code: | DTSC13-306 |
| Subject title: | Advanced Machine Learning |
| Subject level: | Undergraduate |
| Semester/Year: | September 2023 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
|
| Attendance and learning activities: | Attendance at all sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. |
| Prescribed resources: | No Prescribed resources. After enrolment, students can check the Books and Tools area in iLearn for the full Resource List. |
|---|---|
| iLearn@Bond & Email: | iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au |
Enrolment requirements
| Requisites: |
Nil |
|---|---|
| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge. Assumed Prior Learning (or equivalent):Basic Python (equivalent to DTSC11-100) Basic concepts of data science (equivalent to DTSC12-200). |
| Restrictions: |
Nil |
Assurance of learning
Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.
At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.
Program Learning Outcomes (PLOs)
Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Create common models to extract patterns from data using sophisticated machine learning techniques and evaluate their effectiveness.
- Analyse the feasibility and usefulness of predictive models in the context for which they were created.
- Design solutions for common business problems using combinations of machine learning algorithms.
- Use computational infrastructure to implement solutions that involve big data for common business problems.
- Create static and dynamic common visualisations to convey clear information to management and other users.
Generative Artificial Intelligence in Assessment
The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.
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Assessment details
Type Task % Timing* Outcomes assessed Computer-aided Test (Open) Addressing the theoretical contents covered in weeks 1,2 and 3. 20.00% Week 4 1,2,3 Creative Piece Create and assess ensemble models for multiple class problems (supervised learning). 20.00% Week 7 1,2,3,5 Computer-aided Test (Open) Addressing the theoretical contents covered until week 8. 20.00% Week 9 1,2,3 Creative Piece Create and assess a Clustering model for complex problems using sophisticated clustering algorithms (unsupervised learning). 20.00% Week 11 1,2,5 Creative Piece Create and assess a model using big data infrastructure and use advanced visualisation tools to convey information about the model. 20.00% Week 13 1,2,4,5 - * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
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Assessment criteria
Assessment criteria
High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment. Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
| Type | Task | % | Timing* | Outcomes assessed |
|---|---|---|---|---|
| Computer-aided Test (Open) | Addressing the theoretical contents covered in weeks 1,2 and 3. | 20.00% | Week 4 | 1,2,3 |
| Creative Piece | Create and assess ensemble models for multiple class problems (supervised learning). | 20.00% | Week 7 | 1,2,3,5 |
| Computer-aided Test (Open) | Addressing the theoretical contents covered until week 8. | 20.00% | Week 9 | 1,2,3 |
| Creative Piece | Create and assess a Clustering model for complex problems using sophisticated clustering algorithms (unsupervised learning). | 20.00% | Week 11 | 1,2,5 |
| Creative Piece | Create and assess a model using big data infrastructure and use advanced visualisation tools to convey information about the model. | 20.00% | Week 13 | 1,2,4,5 |
- * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
Assessment criteria
| High Distinction | 85-100 | Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. |
|---|---|---|
| Distinction | 75-84 | Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. |
| Credit | 65-74 | Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. |
| Pass | 50-64 | Usually awarded to students whose performance meets the requirements set for work provided for assessment. |
| Fail | 0-49 | Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. |
Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Study Information
Submission procedures
Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
A late penalty will be applied to all overdue assessment tasks unless an extension is granted by the subject coordinator. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.
Academic Integrity
Bond University‘s Student Code of Conduct Policy , Student Charter, Academic Integrity Policy and our Graduate Attributes guide expectations regarding student behaviour, their rights and responsibilities. Information on these topics can be found on our Academic Integrity webpage recognising that academic integrity involves demonstrating the principles of integrity (honesty, fairness, trust, professionalism, courage, responsibility, and respect) in words and actions across all aspects of academic endeavour.
Staff are required to report suspected misconduct. This includes all types of plagiarism, cheating, collusion, fabrication or falsification of data/content or other misconduct relating to assessment such as the falsification of medical certificates for assessment extensions. The longer term personal, social and financial consequences of misconduct can be severe, so please ask for help if you are unsure.
If your work is subject to an inquiry, you will be given an opportunity to respond and appropriate support will be provided. Academic work under inquiry will not be marked until the process has concluded. Penalties for misconduct include a warning, reduced grade, a requirement to repeat the assessment, suspension or expulsion from the University.
Feedback on assessment
Feedback on assessment will be provided to students according to the requirements of the Assessment Procedure Schedule A - Assessment Communication Procedure.
Whilst in most cases feedback should be provided within two weeks of the assessment submission due date, the Procedure should be checked if the assessment is linked to others or if the subject is a non-standard (e.g., intensive) subject.
Accessibility and Inclusion Support
Support is available to students where a physical, mental or neurological condition exists that would impact the student’s capacity to complete studies, exams or assessment tasks. For effective support, special requirement needs should be arranged with the University in advance of or at the start of each semester, or, for acute conditions, as soon as practicable after the condition arises. Reasonable adjustments are not guaranteed where applications are submitted late in the semester (for example, when lodged just prior to critical assessment and examination dates).
As outlined in the Accessibility and Inclusion Policy, to qualify for support, students must meet certain criteria. Students are also required to meet with the Accessibility and Inclusion Advisor who will ensure that reasonable adjustments are afforded to qualifying students.
For more information and to apply online, visit BondAbility.
Additional subject information
As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Subject curriculum
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Introduction to ML
A brief recap on data science and an introduction to advanced machine learning topics. An overview of the current state of machine learning.
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Bagging
The concept of bagging and ensembles of classifiers is provided. The theory behind random forests is presented. Building and tuning random forests.
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Boosting I
An introduction to the concept of boosting and the AdaBoost algorithm. Building and understanding AdaBoost classifiers. Applying AdaBoost models to skewed datasets.
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Boosting II
AdaBoost as a cascade of classifiers. How to train and assess models based on cascades of classifiers.
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SVM
What are support vector machines, and how they work for classification tasks. This technique is compared to other supervised machine learning algorithms.
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Ethics and business practice using ML
Ethical issues related to business practices using machine learning. Common strategies and usage of advanced machine learning in business applications.
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Clustering
Recap on the concepts of unsupervised learning and simple clustering algorithms (K-means). Presenting advanced clustering algorithms, their advantages, and limitations for certain unsupervised problems.
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Big Data processing
Introduce the concept of clusters of computers (Hadoop) and show how they work. Present the concepts of scalability, performance, load balancing, data replication and fault tolerance in Big Data infrastructure.
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Big Data processing II
Basics about programming using PySpark. Understanding how to access services such as AWS and QCIF (Nectar) for big data processing and analysis.
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Visualisation I
Data Visualisation advanced concepts. Problems and pitfalls about visualisation techniques and tools.
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Visualisation II
Visualisations can be static or dynamic (interactive). Students will learn how to use state-of-the-art tools to understand, apply and create dynamic visualisations (e.g., Plotly).
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Reinforcement learning
Concepts of Reinforcement Learning, state space and reward functions. Algorithms Q-learning and SARSA and their advantages and limitations. Applications for reinforcement learning algorithms.