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 2022 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Lecture: x12 (Total hours: 24) - Lecture 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 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 (2017). Deep Learning with Python. n/a, Manning Publications 384
iLearn@Bond & Email: iLearn@Bond is the online learning environment at Bond University and is used to provide access to subject materials, lecture 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 2022 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
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| 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 online learning environment at Bond University and is used to provide access to subject materials, lecture 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:
- Recognise and communicate the inputs, outputs, relationships, boundaries, and data transformations of digital systems.
- Design, train and use neural networks, SVN and ensemble tree models for business data systems.
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- 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 ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.
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% 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% Week 5 2,3,5,6 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% Week 9 1,2,3,5,6 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% Week 12 1,3,4,5,6 - * 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% | 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% | Week 5 | 2,3,5,6 |
| 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% | Week 9 | 1,2,3,5,6 |
| 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% | Week 12 | 1,3,4,5,6 |
- * 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
Unexplained late submissions will not be considered for marks. Penalties will apply for late submissions.
Academic Integrity
University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.
Bond University utilises Originality Reporting software to inform academic integrity.Feedback on assessment
Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.
Accessibility and Inclusion Support
If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.
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 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
- Recognise and communicate the inputs, outputs, relationships, boundaries, and data transformations of digital systems.
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
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Defining Deep Learning
The 4 branches of machine learning are presented. The branches are compared and critiqued. An evaluation of the effectiveness of machine learning models is presented. Recognizing overfitting and underfitting is discussed. Finally a discussion is presented on the progress in AI on a universal workflow of machine learning.
SLOs included
- Recognise and communicate the inputs, outputs, relationships, boundaries, and data transformations of digital systems.
- Design, train and use neural networks, SVN and ensemble tree models for business data systems.
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- 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. Back propagation 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, train and use neural networks, SVN and ensemble tree models for business data systems.
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- 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 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 Back propagation 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 is presented and discussed and contrasted against the hardware requirements for deploying trained networks.
SLOs included
- Design, train and use neural networks, SVN and ensemble tree models for business data systems.
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- 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 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, train and use neural networks, SVN and ensemble tree models for business data systems.
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- 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 statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- 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 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 statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- 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 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, train and use neural networks, SVN and ensemble tree models for business data systems.
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- 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 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
- Recognise and communicate the inputs, outputs, relationships, boundaries, and data transformations of digital systems.
- Design, train and use neural networks, SVN and ensemble tree models for business data systems.
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- 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 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
- Recognise and communicate the inputs, outputs, relationships, boundaries, and data transformations of digital systems.
- Design, train and use neural networks, SVN and ensemble tree models for business data systems.
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- 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 ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.