QBUS6850: Semester 1, 2025
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Unit outline_

QBUS6850: Advanced Machine Learning for Business

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Machine Learning is a crucial component of data analytics that automates analytical model building in modern business contexts. In today’s big data era, the ability to analyse vast and diverse data sources empowers organisations to make informed and data-driven decisions across various business domains. This unit covers a wide range of cutting-edge machine learning algorithms that learn from data, unveiling hidden patterns and relationships critical for strategic business decision making. Potential topics include: Neural Networks, Deep Learning, Text Analytics, Natural Language Processing, Advanced Ensemble Methods, Matrix Decomposition, and Recommender Systems. Emphasis is placed on practical applications involving the analysis of business data, allowing students to develop skills in applying machine learning techniques to solve real-world business challenges.

Unit details and rules

Academic unit Business Analytics
Credit points 6
Prerequisites
? 
QBUS6810
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Junbin Gao, junbin.gao@sydney.edu.au
The census date for this unit availability is 31 March 2025
Type Description Weight Due Length
Supervised exam
? 
Final exam
Closed book Exam
50% Formal exam period 2.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO7
Supervised test
? 
Mid-semester exam
Closed book Exam
25% Week 07
Due date: 12 Apr 2025 at 12:10
1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO7
Assignment group assignment AI Allowed Group project
Computational analysis and written report
25% Week 13
Due date: 26 May 2025 at 16:00

Closing date: 02 Jun 2025
Details in the project document
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
group assignment = group assignment ?
AI allowed = AI allowed ?

Assessment summary

Group project: This assignment will assess students ability to implement, evaluate and analyse machine learning models in an applied setting.

Mid-semester exam: This is a comprehensive exam that covers topics covered in the first half of the semester.

Final exam: This is a comprehensive exam that covers topics covered in the the semester.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator. 

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions. 

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit. 

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply. 

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Following the University/School Late Penalty Policy.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Machine Learning Foundation Lecture (2 hr) LO1 LO2 LO4 LO5
Machine Learning Foundation Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 02 Neural Networks I Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Neural Networks I Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 03 Neural Networks II Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Neural Networks II Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 04 Neural Networks III Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Neural Networks III Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 05 Neural Networks IV Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Neural Networks IV Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 06 Graph Neural Networks Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Graph Neural Networks Practice Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 07 Deep Learning for Text Analytics I Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Deep Learning for Text Analytics I Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 08 Mid-term Exam Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO7
Week 09 Deep Learning for Text Analytics II Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Deep Learning for Text Analytics II Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 10 Deep Learning for Text Analytics III Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Deep Learning for Text Analytics III Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 11 Unsupervised Learning and Clustering Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Unsupervised Learning and Clustering Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 12 Recommendation Systems I Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Recommendation Systems I Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 13 Recommendation Systems II Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Recommendation Systems II Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5

Attendance and class requirements

Lecture recordings: All lectures and seminars are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. differentiate different types of learning algorithms and identify the advantages and limitations of each method
  • LO2. build a strong machine learning skill set for business decision making
  • LO3. create machine learning models for studying relationship amongst business variables
  • LO4. work with various data sets and identify problems within real-world constraints
  • LO5. demonstrate proficiency in the use of statistical software, e.g. Python, for machine learning model implementation
  • LO6. work productively and collaboratively in a team
  • LO7. present and write insights and suggestions effectively, professionally and ethically.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

Weekly schedule has been updated.
  • Main Software: Python is the main software to be utilised in this unit and is available in all the computer labs in the Business School Codrington Building (H69). You are encouraged to use your own computer/laptop. Please refer Canvas site of the unit on how to install the software.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

This unit of study outline was last modified on 07 Feb 2025.

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