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MIS710 - Machine Learning in Business

Year:

2024 unit information

Enrolment modes:

Trimester 1: Burwood (Melbourne), Online
Trimester 2: Burwood (Melbourne), Online, GIFT City (India)^

Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 1: Lemai Nguyen
Trimester 2: Lemai Nguyen
Prerequisite:

Nil

Corequisite:Nil
Incompatible with: Nil
Typical study commitment:

Students will on average spend 150 hours over the trimester period undertaking the teaching, learning and assessment activities.

This will include educator guided online learning activities within the unit site.

Educator-facilitated (scheduled) learning activities - on-campus unit enrolment:

1 x 1.5 hour on-campus (live-streamed) lecture (recordings provided) and 1 x 1.5 hour on-campus seminar each week

Educator-facilitated (scheduled) learning activities - online unit enrolment:

1 x 1.5 hour recorded lecture provided and 1 x 1.5 hour online seminar (recordings provided) each week  

Note:

^GIFT City (India) offering is available to students enrolled at the GIFT City (India) campus only.

Content

Machine Learning allows computers to learn from hidden patterns in big data to quantitatively support business decisions. In this unit, we will cover a large range of methods and algorithms that learn from big data, allowing decision makers to view previously hidden patterns and relationships and build suitable models to support business decision making.

In this unit, students will be introduced to fundamental programming concepts required by business professionals to work with machine learning concepts. This unit introduces machine learning techniques using software package Python, where the emphasis will be on solving business problems using the analysis of business data.

ULO These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: Deakin Graduate Learning Outcomes
ULO1 Analyse and frame business challenges using machine learning concepts, techniques, and the machine learning model development lifecycle.

GLO1: Discipline-specific knowledge and capabilities

GLO3: Digital Literacy
ULO2 Select and apply appropriate machine learning techniques to solve business problems and evaluate the machine learning model performance.  

GLO3: Digital Literacy

GLO5: Problem solving
ULO3 Explain the application of machine learning and interpret the outcomes to the various stakeholders.   GLO2: Communication

Assessment

Assessment Description Student output Grading and weighting
(% total mark for unit)		 Indicative due week
Assessment 1: (Individual) Case study: Data analysis with Written Report (Business)  2000 words 40% Week 5

Assessment 2:

Part A: (Individual) Report (Analytical) 

Part B: (Individual) Report (Business) 

Part A: 2000 words

Part B: 1000 words

Total 60%:

Part A: 40%

Part B: 20%

 Week 11

The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.

Learning Resource

The texts and reading list for the unit can be found on the University Library via the link below: https://deakin.rl.talis.com/modules/MIS710.html Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.

 

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