UnitLevel 3Undergraduate

FIT3191 Generative artificial intelligence

Faculty of Information Technology

FIT3191 Generative artificial intelligence is a level 3, 6-credit-point, undergraduate unit from the Faculty of Information Technology. It isn't offered in 2027. It needs FIT2004 and FIT2112.

Credit points
6
Offered in 2027
Not offered

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Requisites

After FIT3191

No unit lists FIT3191 as a prerequisite in the 2027 handbook.

Overview

This unit covers the theoretical and practical foundations of Generative Artificial Intelligence (GenAI), building on prior knowledge of deep learning. The unit begins with essential Natural Language Processing (NLP) concepts that underpin modern generative systems, such as text representation, contextual embeddings, and sequence modelling. You then progress to large language models (LLMs), advanced generative techniques including variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models, as well as their integration into real-world applications such as conversational systems, summarisation, translation, and multimodal AI.

Throughout the unit, you will critically evaluate the architectures and training strategies that power generative systems, with strong emphasis on ethical, societal, and regulatory considerations. By the end of the unit, you will be able to design, apply, and evaluate generative AI solutions responsibly across various complex domains.

Offerings in 2027

The 2027 handbook lists no offerings for FIT3191.

Learning outcomes

When you finish this unit, you should be able to:

  1. 1

    Explain the core principles of NLP and Generative AI, and how these methods enable artificial intelligence systems to interpret and generate human language.

  2. 2

    Analyse the architectures and training strategies that underpin large language models and other generative approaches.

  3. 3

    Apply NLP and Generative AI in practical applications such as dialogue systems, translation, summarisation, and multimodal systems.

  4. 4

    Evaluate generative models using theoretical foundations and appropriate metrics, considering accuracy, creativity, robustness, and limitations.

  5. 5

    Assess the ethical, legal, and societal implications of NLP and generative AI technologies, including bias, fairness, safety, and governance.

Where it fits

FIT3191 is part of 4 areas of study in the 2027 handbook.

Contacts

Chief Examiners
Dr Vishnu Monn

Common questions

What are the prerequisites for FIT3191?

You need FIT2004 and FIT2112 before you enrol.

When is FIT3191 offered?

FIT3191 has no offerings listed in the 2027 handbook.

Which majors and minors include FIT3191?

FIT3191 is part of Artificial intelligence; Artificial intelligence algorithms and models; Data science; and Software engineering.

More details

Credit points
6
Level
3
Study level
Undergraduate
Faculty
Faculty of Information Technology
Type
Coursework
EFTSL
0.125
Student contribution
SCA Band 2
Study abroad
Available
Handbook years
20262027