Artificial Intelligence and Insights | Units of study... | Swinburne University of Technology

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INF80051 12.5 Credit Points Hawthorn Available to incoming Study Abroad and Exchange students

Contact hours

36 hours

On-campus unit delivery combines face-to-face and digital learning.

2023 teaching periods

Hawthorn HB6 HE Block 6
Dates: 21 Aug 23 - 1 Oct 23 Results: 31 Oct 23 Last self enrolment: 21 Aug 23 Census: 1 Sep 23 Last withdraw without fail: 15 Sep 23

Hawthorn

HB6 HE Block 6

Dates:
21 Aug 23 - 1 Oct 23

Results:
31 Oct 23

Last self enrolment:
21 Aug 23

Census:
1 Sep 23

Last withdraw without fail:
15 Sep 23

Prerequisites

INF70008 Business Analytics and Visualisation

Aims and objectives

This unit develops and enhances students’ conceptual and practical understanding of artificial intelligence (and in particular machine learning) in various business contexts where artificial intelligence has emerged to play a crucial role. Students learn about fundamental concepts, key techniques and popular tools in artificial intelligence and learn how it is influencing various businesses and industries. Students also acquire the ability to apply artificial intelligence techniques and tools to resolve issues and problems arising in different business contexts.

Students who successfully complete this unit will be able to:

1. Demonstrate advanced knowledge of fundamental concepts, key techniques and popular tools in machine learning
2. Critically evaluate the roles of machine learning in various business contexts
3. Critically analyse, evaluate and apply appropriate machine learning techniques and tools to solve various business problems
4. Communicate effectively as a professional and function as an effective leader or member of a team

Unit information in detail

- Teaching methods, assessment, general skills outcomes and content.

Teaching methods

Face to Face:

Class 36 hours (12 x 3 hours)

Block Mode:

This unit may also be delivered in block or intensive mode. Block mode may consist of blended, intensive and immersive teaching, which can include full day teaching and weekend teaching requirements.

Student workload:

Independent Learning

114 hours (12 x 9.5 hours)

For all teaching and learning structures (both face to face and online), students are expected to spend an average of 150 hours per unit in total over the duration of the study period
This includes all:

Scheduled teaching and learning events and activities (contact hours timetabled in a face-to-face teaching space) and scheduled online learning events (contact hours scheduled in an online teaching space), and
Non-scheduled learning events and activities (including directed online learning activities, assessments, independent study, student group meetings, and research)

To be successful, students should:

Read all prescribed materials and/or view videos in preparation for each class
Attend and engage in all scheduled classes (face to face or online)
Start assessment tasks well ahead of the due date, and submit assessments promptly
Read / listen to all feedback carefully, and consider it for future assessment
Engage with fellow students and teaching staff (don’t hesitate to ask questions)

Assessment

Report (Individual) 20% - 30%
Assignment (Individual) 20% - 30%
Case study and presentation (Group) 40% - 60%

General skills outcomes

Teamwork skills
Problem solving skills
Analysis skills
Communication skills
Ability to tackle unfamiliar problems
Ability to work independently

Content

Foundations of artificial intelligence (especially machine learning)
Machine learning applications in FinTec
Key machine learning techniques: supervised, unsupervised and semi-supervised
Popular machine learning tools and computing platforms
Applications of machine learning to representative financial services such as automated trading and roboadvice

Study resources

- Reading materials and recommended reading.

Reading materials

Reference material for this course will comprise contemporary readings in the research literature, industry publications and the business press.