SIG788 - Engineering AI Solutions
| Year: | 2024 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Great Learning |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Prerequisite: | Nil |
| Corequisite: | Must be enrolled in S773 Master of Data Science (Global) |
| Incompatible with: | SIT788 |
| Study commitment | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. This will include educator guided online learning activities within the unit site. |
| Scheduled learning activities - online | Online independent and collaborative learning including optional scheduled activities as detailed via the Great Learning platform. |
| Note: | This unit is part of the Master of Data Science (Global) program and is restricted to online international students who reside outside Australia. |
Content
AI is changing the world we live in and every new technology advance is based on the advances of machine learning. In this unit you will go beyond the algorithms and learn how to build, develop and deploy AI solutions. A core characteristic of AI is understanding how it differs from traditional software projects and the different tasks that are required throughout the software development lifecycle. Due to the inherently probabilistic nature of AI Solutions, practitioners need new tools and approaches to assist them in building robust solutions. In this unit you will learn how to manage challenges that arise when building AI Solutions and gain a deep understanding of how to overcome these challenges. Throughout the course you will learn how to plan for and design the core elements of an AI Solution and guided through the development from concept all the way through to a deployable artefact. These skills will prepare you for the workforce ensuring that you have a strong vocabulary for communicating efficiently with project stakeholders including research engineers, software engineers and project managers. At the conclusion of this unit you will be equipped to make strong contributions to the development of new AI technologies.
Hurdle requirement
To be eligible to obtain a pass in this unit, students must meet certain milestones as part of the portfolio.