General Information
Unprecedented volumes of data are being created on an almost daily basis and the amount of data we generate is expected to double every two years. This ‘Big Data’ has the power to change the way we work, live, and think. This subject is designed to provide students with the knowledge and skills to analyse Big Data in a variety of business contexts. Specifically, mathematical and practical applications of Artificial Neural Networks, Support Vector Machines, Natural Language Processing and Ensemble Decision Tree techniques are explored. Valuable skills in the use of these techniques are reinforced with practical application.
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Details
Academic unit: Bond Business School Subject code: DTSC13-301 Subject title: Applied Machine Learning Subject level: Undergraduate Semester/Year: May 2023 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Forum: x12 (Total hours: 24) - Forum
- Computer Lab: x12 (Total hours: 24) - Computer Lab
- Personal Study Hours: x12 (Total hours: 72) - Recommended study time & reviewing materials
Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. -
Resources
Prescribed resources: Books
- Francois Chollet (2021). Deep Learning with Python, Second Edition. n/a, Simon and Schuster 502
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: | DTSC13-301 |
| Subject title: | Applied Machine Learning |
| Subject level: | Undergraduate |
| Semester/Year: | May 2023 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
|
| Attendance and learning activities: | Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. |
| Prescribed resources: | Books
|
|---|---|
| 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: |
Pre-requisites:Co-requisites:There are no co-requisites |
|---|---|
| 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):Possess demonstrable knowledge in elementary probability theory, statistics, elementary calculus and linear algebra to the level of a unit such as STAT11-112 Quantitative Methods. |
| 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:
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- Design and train neural networks, including convolutional and recurrent structures and other modern extensions.
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
- Apply the communication framework for translating data analysis into decision making outcomes.
- Articulate machine learning ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.
- Demonstrate an appropriate awareness of global issues impacting decision-making paradigms and model building exercises.
- Apply appropriate professional standards and best practices to make ethical, responsible decisions decision-making paradigms and model building exercises.
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 Examination (Open) Comprehensive final examination 40.00% Final Examination Period 1,2,3,4,5,6 Written Report ANN Assignment. This assignment requires students to write an academic paper. The contribution to the literature is stylized to be the objective reporting of the training and testing of a Deep Learning Network. 20.00% Week 5 1,2,5,6 Written Report Machine Learning for Business. This assignment asks students to prepare a business report as a consultant. Data collection and analysis must be conveyed succinctly in the Executive Summary. 20.00% Week 9 1,2,3,5,6,7,8 Written Report Machine Learning Techniques. For this task, students will analyse the same set of business data by a variety of techniques and present the comparative results. 20.00% Week 12 2,3,4,5,6,7,8 - * 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 Examination (Open) | Comprehensive final examination | 40.00% | Final Examination Period | 1,2,3,4,5,6 |
| Written Report | ANN Assignment. This assignment requires students to write an academic paper. The contribution to the literature is stylized to be the objective reporting of the training and testing of a Deep Learning Network. | 20.00% | Week 5 | 1,2,5,6 |
| Written Report | Machine Learning for Business. This assignment asks students to prepare a business report as a consultant. Data collection and analysis must be conveyed succinctly in the Executive Summary. | 20.00% | Week 9 | 1,2,3,5,6,7,8 |
| Written Report | Machine Learning Techniques. For this task, students will analyse the same set of business data by a variety of techniques and present the comparative results. | 20.00% | Week 12 | 2,3,4,5,6,7,8 |
- * 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 Lead Educator. 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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Deep Learning and Artificial Neural Networks
A brief history of machine learning prior to Deep Learning is covered. Statistical learning is introduced and the distinction between regression, classification and clustering in the context of Machine Learning is explained. Mathematical models of artificial neural networks are introduced. Emphasis is placed on the inherent non-linearity of Artificial Neural Networks. The relationship between AI, Machine Learning and Deep Learning is explored.
SLOs included
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
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Defining Deep Learning
The four branches of machine learning are presented. The branches are compared and critiqued. An evaluation of the effectiveness of machine learning models is presented. Recognising overfitting and underfitting is discussed. Finally, a discussion is presented on the progress in AI on a universal workflow of machine learning.
SLOs included
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- Design and train neural networks, including convolutional and recurrent structures and other modern extensions.
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
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Neural Networks
Applying classic ANN’s to practical problems is discussed. Empirical determination of Neural Network topologies is discussed and a heuristic derived for the number of nodes in the hidden layer given the training data available and dimensionality of the problem. Backpropagation and ELM (Extreme Learning Machines) training methodologies are mathematically exampled. The evaluation of the training of ANN’s for each method is compared and contrasted. The concepts and tools to recognize underfitting and overfitting are introduced.
SLOs included
- Design and train neural networks, including convolutional and recurrent structures and other modern extensions.
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
- Apply the communication framework for translating data analysis into decision making outcomes.
- Articulate machine learning ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.
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Deep Learning - MLP
Multi Layered Perceptrons (MLP) are covered in detail. The limitations of the ELM methodology provides a natural discussion of the domination of Backpropagation training techniques. A detailed discussion of the evolution of implementation techniques this millennium is presented, as is the terminology of Deep Learning. The hardware requirements for training Deep Learning models are presented, discussed, and contrasted against the hardware requirements for deploying trained networks.
SLOs included
- Design and train neural networks, including convolutional and recurrent structures and other modern extensions.
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
- Apply the communication framework for translating data analysis into decision making outcomes.
- Articulate machine learning ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.
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Tensor Flow, Keras and CNN
Tensor Flow is discussed and its relationship to Keras explained. The structural characteristics of a Convolutional Neural Network (CNN) are defined and evaluated against MLPs. Implementation of a Deep Learning architecture involving CNNs and MLPs in Keras via Python are presented. Representations of such models for computer vision in Keras are presented and discussed.
SLOs included
- Design and train neural networks, including convolutional and recurrent structures and other modern extensions.
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
- Apply the communication framework for translating data analysis into decision making outcomes.
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Natural Language Processing: Theory, Application and Limitations
A history of the development of NLP is covered. An introduction to the vast areas of business applications of NLP in Python is discussed. Emphasis is placed on the business applications of sentiment analysis. The state of the art of sentiment analysis in the currently available Python packages is examined.
SLOs included
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
- Apply the communication framework for translating data analysis into decision making outcomes.
- Articulate machine learning ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.
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Ongoing and Future Research in NLP
Current research issues and ongoing developments in the field of NLP and text processing are presented and discussed in detail. It is shown that the AI Complete problem may never be fully resolved. Theoretical arguments of this are presented.
SLOs included
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
- Apply the communication framework for translating data analysis into decision making outcomes.
- Articulate machine learning ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.
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Deep Learning applied to Text and Sequences
Application of Deep Learning models to work with textual data is presented with applications. OHE of words and characters is explained in detail. An understanding of LSTM Deep Learning Models is presented. Using recurrent dropout to combat overfitting is examined with applications. The theory of bidirectional RNN and the stacking of recurrent layers is discussed.
SLOs included
- Design and train neural networks, including convolutional and recurrent structures and other modern extensions.
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
- Apply the communication framework for translating data analysis into decision making outcomes.
- Articulate machine learning ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.
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Comparative Case Study
A comparison of the strengths and weaknesses of a number of Deep Learning Techniques is presented. Which technique should be used where and when?
SLOs included
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- Design and train neural networks, including convolutional and recurrent structures and other modern extensions.
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
- Apply the communication framework for translating data analysis into decision making outcomes.
- Articulate machine learning ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.
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Data Science, Machine Learning and Business Strategy
A review of the area of machine learning, inference and application to solving business problems is discussed. Case studies are referenced and open for discussion. The currently topical area of the application of AI in business is examined.
SLOs included
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- Design and train neural networks, including convolutional and recurrent structures and other modern extensions.
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
- Apply the communication framework for translating data analysis into decision making outcomes.
- Articulate machine learning ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.