Emerging Technologies: Artificial Intelligence Applications

Syllabus

• Week 1

  Foundations of artificial intelligence (AI) and machine learning (ML)

  • Let's get started

    Meet your teaching team and other learners, and find key information about this course, including useful tips and links to help you get the most out of your learning.

  • Introduction to AI

    A brief history and different approaches to AI.

  • Machine learning (ML) and deep learning

    Delving into machine learning, including supervised and unsupervised approaches, while examining how neural networks operate and how deep architectures are leveraged across various domains.

  • Reinforcement learning (RL) and representation learning

    Learning about reinforcement learning (RL) and representation learning, their algorithms and applications.

  • Weekly wrap

    Reflect on the key ideas presented this week and find out what's coming up in Week 2.

• Week 2

  Computer vision: techniques and applications

  • Computer vision and traditional AI techniques

    Learning what computer vision is, how it is applied in real world, and what AI techniques were traditionally used for computer vision.

  • Deep learning for computer vision

    Learning about neural networks in computer vision and the different deep learning techniques that can be used in this space.

  • Weekly wrap

    Reflect on the key ideas presented this week and find out what’s coming up in Week 3.

• Week 3

  Understanding language with AI - from text to transformers

  • Introduction to NLP and text analysis

    Learning about NLP concepts, real-world relevance, and the importance of preprocessing text to prepare it for AI analysis.

  • Representing and understanding language

    Exploring how machines represent, interpret, and extract meaning from language using structured techniques like embeddings, tagging, and parsing.

  • Language tasks and applications

    Focusing on practical NLP tasks that involve generating or extracting meaningful responses from text, such as translation, summarisation, and QA systems.

  • Sequence models and Transformer architectures

    Covering the evolution of sequence modelling in NLP, from RNNs to Transformers, explaining how modern models handle context, memory, and long-range dependencies.

  • Weekly wrap

    Reflect on the key ideas presented this week and find out what's coming up in Week 4.

• Week 4

  Generative AI: concepts, models, and applications

  • Foundations of generative AI

    Learning what generative AI is, how it differs from other forms of AI such as discriminative and agentic AI, and exploring some of its applications

  • Core model architectures

    Exploring the main generative architectures, each with unique strengths for learning and creating new data.

  • Advanced generative systems

    Covering modern large-scale generative models, their multimodal capabilities, and methods for aligning them to real-world tasks.

  • Weekly wrap

    Reflect on the key ideas presented this week and find out what’s coming up in Week 5.

• Week 5

  Time series and ethical AI in business

  • Foundations of time series

    Exploring what time series data is, its key characteristics, and its components, highlighting how trends, seasonality, cycles, and noise shape business insights and decision-making.

  • Traditional time series techniques

    Learning about statistical methods and exponential smoothing for time series.

  • Classical machine learning for time series

    Exploring feature leaning and machine learning models for analysing and predicting time series data.

  • Deep learning for time series

    Learning about deep learning for time series and models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs).

  • Ethics and responsible AI in business

    Focusing on privacy, ethics, and governance when applying AI to business problems, especially in the Australian context.

  • Weekly wrap

    Reflect on the key ideas presented this week and find out what is coming up in Week 6.

• Week 6

  Assessment, summary, and next steps

  • Assessment preparation and submission

    Learn practical tips and information about how to complete and submit your assessment. Reflect on what you've learnt throughout this course, and how you'll integrate this into your assessment task.

  • Course wrap

    Reflect on what you’ve learnt over the duration of this course, and find out more about how to use your credential before considering the next steps of your learning journey.

Who will you learn with?