QBUS6850: Semester 2, 2022

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Units QBUS6850 Semester 2 2022 [Normal day]

Unit of study_

QBUS6850: Machine Learning for Business

Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney

Overview

Machine Learning is a fundamental aspect of data analytics that automates analytical model building in modern business. In the big data era, managers are able to use very large and rich data sources and to make business decisions based on quantitative data analysis. Machine Learning covers a range of state-of-the-art methods/algorithms that iteratively learn from data, allowing computers to find hidden patterns and relationships in such data so as to support business decisions. This unit introduces modern machine learning techniques and builds skills in using data for everyday business decision making. Topics include: Machine Learning Foundation; Modern Regression Methods; Advanced Classification Techniques; Latent Variable Models; Support Vector Machines (SVM) and Kernel Methods; Artificial Neural Networks; Deep Learning; and Machine Learning for Big Data. Emphasis is placed on applications involving the analysis of business data. Students will practise applying machine learning algorithms to real-world datasets by using an appropriate computing package.

Unit details and rules

Unit code	QBUS6850
Academic unit	Business Analytics
Credit points	6
Prohibitions ?	None
Prerequisites ?	QBUS6810
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Stephen Tierney, stephen.tierney@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam n/a	50%	Formal exam period	2 hours
Outcomes assessed:
In-semester test (Record+)	Mid-semester exam n/a	25%	Week 07 Due date: 12 Sep 2022 at 13:00	1.5 hours
Outcomes assessed:
Assignment	Group project Computational analysis and written report	25%	Week 13 Due date: 06 Nov 2022 at 16:00 Closing date: 13 Nov 2022	details in the project document
Outcomes assessed:
= group assignment ? = Type B final exam ? = Type B in-semester exam ?

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 2014 (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 sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

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.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Neural Networks 1	Lecture (2 hr)
Week 02	Neural Networks 2	Lecture (2 hr)
Week 02	Neural Networks 1	Tutorial (2 hr)
Week 03	Neural Networks 3	Lecture (2 hr)
Week 03	Neural Networks 2	Tutorial (2 hr)
Week 04	Neural Networks 4	Lecture (2 hr)
Week 04	Neural Networks 3	Tutorial (2 hr)
Week 05	Neural Networks 5 - Advanced or Recent Topic	Lecture (2 hr)
Week 05	Neural Networks 4	Tutorial (2 hr)
Week 06	Advanced Classification Techniques 1	Lecture (2 hr)
Week 06	Neural Networks 5 - Advanced or Recent Topic	Tutorial (2 hr)
Week 07	Mid-term Exam	Lecture and tutorial (4 hr)
Week 08	Advanced Classification Techniques 2	Lecture (2 hr)
Week 08	Advanced Classification Techniques 1	Tutorial (2 hr)
Week 09	Advanced Classification Techniques 3	Lecture (2 hr)
Week 09	Advanced Classification Techniques 2	Tutorial (2 hr)
Week 10	Recommendation Systems 1	Lecture (2 hr)
Week 10	Advanced Classification Techniques 3	Tutorial (2 hr)
Week 11	Recommendation Systems 2	Lecture (2 hr)
Week 11	Recommendation Systems 1	Tutorial (2 hr)
Week 12	Advanced or Recent Topic in Machine Learning	Lecture (2 hr)
Week 12	Recommendation Systems 2	Tutorial (2 hr)
Week 13	Revision	Lecture (2 hr)
Week 13	Advanced or Recent Topic in Machine Learning	Tutorial (2 hr)

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

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.

Outcomes
Graduate qualities

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

LO1. understand the 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 models 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
Learning outcomes

Responding to student feedback

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

Order of modules has been changed.

Additional information

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.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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