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DTSC71-301: Applied Machine Learning May 2022 [Standard]

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.


Academic unit:Bond Business School
Subject code:DTSC71-301
Subject title:Applied Machine Learning
Subject level:Postgraduate
Semester/Year:May 2022
Credit points:10

Delivery & attendance

Delivery mode:


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.


Prescribed resources:
  • Francois Chollet (2017). Deep Learning with Python. Manning Publications , 384.
After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.
[email protected] & Email:[email protected] 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

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 STAT71-112 Quantitative Methods

Restrictions: ? This subject is not available to
  • Study Abroad Students

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.

Find your program

Subject Learning Outcomes (SLOs)

On successful completion of this subject the learner will be able to:
  1. Recognise and communicate the inputs, outputs, relationships, boundaries, and data transformations of digital systems.
  2. Design, train and use neural networks, SVN and ensemble tree models for business data systems.
  3. Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
  4. Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
  5. Apply the communication framework for translating data analysis into decision making outcomes.
  6. Articulate ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.


Assessment details

TypeTask%Timing*Outcomes assessed
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, 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.
Computer-Aided Examination (Open) Comprehensive final examination 40% Final Examination Period 1, 2, 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 [email protected] 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.

Policy on plagiarism

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

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.

1, 3, 4.

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.

1, 2, 3, 4.

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.

2, 3, 4, 5, 6.

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.

2, 3, 4, 5, 6.

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.

2, 3, 4, 5.

A history of the development of NLP is covered. An introduction to the vast areas of business applications of NLP in R 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 and R packages is examined.

3, 4, 5, 6.

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.

3, 4, 5, 6.

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.

2, 3, 4, 5, 6.

A comparison of the strengths and weaknesses of a number of Deep Learning Techniques is presented. Which technique should be used where and when?

1, 2, 3, 4, 5, 6.

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.

1, 2, 3, 4, 5, 6.
Approved on: Mar 14, 2022. Edition: 2.4