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Unit of study_

MKTG6010: Machine Learning in Marketing

With fast growth of the modern digital economy, multi-quintillion bytes of data is generated every day. This provides an enormous opportunity and a significant challenge for marketers to extract marketing insights because of not only the size of the data, but also the structure of the data. A growing proportion of the data is unstructured, such as customer emails and texts, mobile data, social media UGCs, C2C data on two-sided platforms in the sharing economy. Traditional marketing research methods cannot be used to solve these problems. This unit introduces state of the art machine learning methods to help marketers extract consumers insights from big data including structured and unstructured data and make better informed business decisions.

Details

Academic unit Marketing
Unit code MKTG6010
Unit name Machine Learning in Marketing
Session, year
? 
Semester 1, 2021
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Prohibitions
? 
None
Prerequisites
? 
BUSS6002
Corequisites
? 
None
Available to study abroad and exchange students

Yes

Teaching staff and contact details

Coordinator Steven Lu, steven.lu@sydney.edu.au
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final exam
Written exam
30% Formal exam period 2 hours
Outcomes assessed: LO1 LO4 LO3 LO2
Presentation group assignment Presentation
n/a
10% Multiple weeks 15 minutes
Outcomes assessed: LO1 LO2 LO3 LO4
Small continuous assessment Class participation
n/a
15% Multiple weeks As needed
Outcomes assessed: LO1 LO4
Assignment group assignment Term project 1
n/a
20% Week 07 1500 words
Outcomes assessed: LO1 LO3 LO2
Assignment group assignment Term project 2
n/a
25% Week 13 2000 words
Outcomes assessed: LO1 LO4 LO3 LO2
group assignment = group assignment ?
Type C final exam = Type C final exam ?

Term project stage 1

Term project stage 2

Presentation

In-semester assessment

Final exam:

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.

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

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.

WK Topic Learning activity Learning outcomes
Week -02 Common Methods in Marketing Machine Learning Lecture and tutorial (3 hr)  
Week 01 Introduction to Marketing Machine Learning (MML) Lecture and tutorial (3 hr)  
Week 03 Modelling Choice Behaviour I Lecture and tutorial (3 hr)  
Week 04 Modelling Choice Behaviour II Lecture and tutorial (3 hr)  
Week 05 Modelling Choice Behaviour III Lecture and tutorial (3 hr)  
Week 06 Modelling Choice Behaviour IV Lecture and tutorial (3 hr)  
Week 07 Text and Voice Analytics for Marketing Applications I Lecture and tutorial (3 hr)  
Week 08 Text and Voice Analytics for Marketing Applications II Lecture and tutorial (3 hr)  
Week 09 Text and Voice Analytics for Marketing Applications III Lecture and tutorial (3 hr)  
Week 10 Marketing Network Analysis Lecture and tutorial (3 hr)  
Week 11 Image and Video Analytics for Marketing Applications Lecture and tutorial (3 hr)  
Week 12 Presentation Lecture and tutorial (3 hr)  
Week 13 Revision Lecture and tutorial (3 hr)  

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. Apply machine learning methods commonly used in marketing and evaluate the advantages and disadvantages of those methods
  • LO2. Identify the machine learning models required to analyze practical problems in marketing
  • LO3. Apply machine learning to extract insights from big data for better decision making
  • LO4. Apply the required analytical techniques across marketing units

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 is the first time this unit has been offered

Disclaimer

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

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