Global Masters in Applied AI from Deakin University

This preparatory module will introduce you to the world of data and AI, provide an overview of how problems are solved in the industry using data and AI, and give you a fundamental understanding of the hands-on tools needed to build a strong foundation for Generative AI applications.

This preparatory module will introduce you to the world of data and AI, provide an overview of how problems are solved in the industry using data and AI, and give you a fundamental understanding of the hands-on tools needed to build a strong foundation for Generative AI applications.

In this module, you will learn to read, explore, manipulate, and visualize data to tell stories, solve business problems, and deliver actionable insights and recommendations by performing exploratory data analysis using some of the most widely used Python packages.

This module is designed to help you build an understanding of the concept of learning from data, develop linear and non-linear models to capture relationships between attributes and known outcomes, and discover patterns in and segment data with no labels.

This module focuses on exploring how to combine the decisions from multiple models using ensemble techniques to improve performance and make better predictions, while applying feature engineering and hyperparameter tuning to build generalized, robust models that optimize associated business costs.

This module helps you implement neural networks to synthesize knowledge from data, understand different optimization algorithms and regularization techniques, and evaluate factors that improve performance, enabling you to build generalized and robust neural network models to solve business problems.

This module helps you get introduced to the world of Natural Language Processing (NLP), gain a practical understanding of text embedding methods, and learn how different transformer architectures power Large Language Models (LLMs). You will explore how Retrieval-Augmented Generation (RAG) integrates information retrieval to improve the accuracy and relevance of LLM responses, and design and implement robust NLP solutions using open-source LLMs combined with prompt engineering techniques.

This module introduces you to the shift from traditional automation to Agentic AI. You will learn how to build intelligent agents using LangChain, equip them with dynamic tool-use capabilities, integrate memory into AI agents, and understand the mechanics of planning, multi-step reasoning, and the ReAct framework to enable agents to decompose and solve complex, multi-stage tasks. Finally, you will learn to evaluate AI agents to develop reliable AI solutions enhanced with human oversight.

This module helps you understand the role of model deployment in realizing the value of an ML model and teaches you how to build and deploy an application using Python.

This asynchronous module helps you explore how to solve business problems by generating code using Generative AI tools, examine the capabilities of text-to-image and image-to-text GenAI tools like DALL·E through business use cases, and understand the speech recognition capabilities of audio-to-text GenAI tools like Whisper in practical business applications.

This module introduces you to the world of computer vision, helps you understand image processing and various methods for extracting informative features from images, and guides you in building Convolutional Neural Networks (CNNs) to uncover hidden patterns in image data and solve image classification problems at your own pace.

This module helps you perform statistical analysis using Python to evaluate the reliability of business estimates through confidence intervals and hypothesis testing. You will learn to analyze data distributions, test assumptions before committing resources, and make informed decisions based on data-driven evidence.

This module helps you understand the core concepts of databases and SQL, gain hands-on experience writing simple SQL queries to filter, manipulate, and retrieve data from relational databases, and use advanced SQL techniques such as joins, window functions, and subqueries to solve real-world data problems and extract actionable business insights.

This module introduces you to recommendation systems and guides you in building models that leverage past product purchase and satisfaction data to deliver high-quality, personalized recommendations.

Reinforcement Learning (RL) Key Highlights: • Work with MDP variants such as discrete-time MDPs, Semi-MDPs (SMDP), continuous-time MDPs, POMDPs, and MOMDPs • Apply core RL techniques, including multi-armed bandits, value iteration, policy gradient, temporal difference learning, and reward design • Understand advanced concepts such as on-policy vs off-policy learning, eligibility traces, feature construction, and continuous action spaces • Explore emerging areas, including deep RL, multi-agent systems, transfer learning, hierarchical and curiosity-driven learning Engineering AI Solutions Key Highlights: • Understand the process and key characteristics of developing AI solutions and how they differ from traditional software development • Design, develop, deploy, and maintain AI solutions using modern tools, frameworks, and libraries • Apply engineering principles and the scientific method with appropriate rigour in experimentation • Manage stakeholder expectations and guide the operationalisation of AI solutions from inception to deployment and ongoing maintenance

Robotics, Computer Vision and Speech Processing Key Highlights: • Understand how computer vision and speech processing enable sensing and interaction in robotics • Analyse existing algorithms and their applications in real-world robotic scenarios • Investigate state-of-the-art machine learning techniques used in vision and speech domains • Develop novel solutions integrating computer vision and speech processing for robotics applications Deep Learning Key Highlights: • Understand core deep learning theories, including computational graphs and representation learning • Build deep learning models for structured and unstructured data • Learn key techniques such as convolutional neural networks, recurrent networks, and neural embedding methods • Explore real-world applications of deep learning widely adopted across industries