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Master of Applied Artificial Intelligence (Global)

Master of Applied Artificial Intelligence (Global)

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Application closes 15th Apr 2026

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Program Outcomes

Elevate your expertise with Applied AI skills

Build advanced AI skills across algorithm design, deployment & human-aligned systems

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    Develop strong mathematical and statistical foundations to understand and build modern AI algorithms

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    Design, develop, and deploy AI solutions from concept to deployable artefact

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    Apply Deep Learning techniques to structured and unstructured data, including images, videos, and text

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    Implement Reinforcement Learning methods across complex decision environments

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    Build ethical, safe, explainable, and human-aligned AI systems

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    Develop novel AI solutions in Computer Vision, Speech Processing, and Robotics

Key program highlights

Why choose this masters degree?

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    A global masters degree & PG certificates

    Gain the recognition of a global masters degree and PG certificates from global universities at just 1/10th the cost of a 2 year on-campus masters

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    Practical, hands-on learning from world-class faculty

    Live virtual classes, Industry sessions and competency courses delivered by experts and faculty at Deakin

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    Industry-Focussed Curriculum

    The curriculum emphasises practical skills and real-world problem-solving, covering advanced topics such as Robotics and Human-Aligned AI

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    Dedicated career support

    Get expert guidance to prepare for job roles with mock interviews, resume building, and e-portfolio review

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    Connect with your alumni community

    Join the alumni portal with over 300,000 Deakin graduates, reconnect, and meet with fellow alumni across the globe

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    11+ hands-on projects & 27+ tools

    The program includes 11+ hands-on projects, 60+ case studies, 1 capstone project, and 27+ tools to strengthen practical and conceptual knowledge

Skills you will learn

Reinforcement Learning

Computer Vision

Speech Processing

Deep Learning

Human Aligned AI

Mathematics for AI

AI Solution Engineering

Ethical & Explainable AI

Robotics

Deployable AI Systems

Reinforcement Learning

Computer Vision

Speech Processing

Deep Learning

Human Aligned AI

Mathematics for AI

AI Solution Engineering

Ethical & Explainable AI

Robotics

Deployable AI Systems

view more

  • Overview
  • Curriculum
  • Faculty
  • Fees
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This program is ideal for

The Master of Applied Artificial Intelligence (Global) helps you align your learning with your career goals

  • Early-Career Professionals

    Strengthen your foundation in Applied AI and build the expertise needed to grow in technical roles

  • Mid–Senior Professionals

    Enhance your AI capabilities to advance into strategic and innovation-driven roles

  • Non-tech professionals

    Develop structured knowledge in Applied AI to pivot into AI-focused career paths

  • Tech Leaders

    Lead AI-driven initiatives with advanced knowledge and strategic insight

Curriculum

The curriculum, designed and delivered by faculty experts, emphasises practical, industry-relevant skills

Year 1 (PGP-AIML)

PGP-AIML CURRICULUM The curriculum of the PGP in Artificial Intelligence & Machine Learning has been updated in consultation with industry experts, academicians & program alums to ensure you learn the most cutting-edge topics. Python and GenAI Prep Work: 1) Python Bootcamp for Non-programmers 2) Python Prep Work 3) Generative AI Prep Work

Course 01: Introduction to Python

This course guides you to read, explore, manipulate, and visualize data to tell stories, solve business problems, and deliver actionable insights and business recommendations by performing exploratory data analysis using some of the most widely used Python packages. •Introduction to Python •Data Manipulation •Exploratory Data Analysis

Course 02: Machine Learning

This course helps you build an understanding of the concept of learning from data, build linear and non-linear models to capture the relationships between attributes and a known outcome, and discover patterns in and segment data with no labels. •Linear Regression •Decision Trees •K-means Clustering

Course 03: Advanced Machine Learning

This course helps you explore how to combine the decisions from multiple models using ensemble techniques to improve model performance and make better predictions, and employ feature engineering techniques and hyperparameter tuning to arrive at generalized, robust models to optimize associated business costs. •Bagging •Boosting •Model Tuning

Course 04: Introduction to Neural Networks

This course helps you implement neural networks to synthesize knowledge from data, demonstrate an understanding of different optimization algorithms and regularization techniques, and evaluate the factors that contribute to improving performance to build generalized and robust neural network models to solve business problems. •Introduction to Neural Networks •Optimizing Neural Networks

Course 05: Natural Language Processing with Generative AI

This course will help you get introduced to the world of natural language processing, gain a practical understanding of text embedding methods, gain a practical understanding of the working of different transformer architectures that lie at the core of large language models (LLMs), explore how retrieval augmented generation (RAG) integrates information retrieval to improve the accuracy and relevance of responses from an LLM, and design and implement robust NLP solutions using open-source LLMs combined with prompt engineering techniques. •Word Embeddings •Attention Mechanism and Transformers •Large Language Models and Prompt Engineering •Retrieval Augmented Generation

Course 06: AI Agents for Automation

This course introduces participants to the shift from traditional automation to the world of Agentic AI, covering how to build intelligent agents using LangChain, equip them with dynamic tool-use capabilities, integrate memory into AI agents, understand the mechanics of planning and the ReAct framework to enable agents to decompose and solve complex, multi-stage tasks. Finally, you will learn to evaluate AI agents to enable reliable AI solutions enhanced with human oversight. •Introduction to AI Agent Workflows •Planning and Reasoning in AI Agents •Evaluating AI Agents

Course 07: Model Deployment

This course will help you comprehend the role of model deployment in realizing the value of an ML model and how to build and deploy an application using Python. •Introduction to Model Deployment •Containerization

Course 08: Introduction to SQL

This course will help you gain an understanding of the core concepts of databases and SQL, gain practical experience writing simple SQL queries to filter, manipulate, and retrieve data from relational databases, and utilize complex SQL queries with joins, window functions, and subqueries for data extraction and manipulation to solve real-world data problems and extract actionable business insights. •Data Retrieval & Aggregation Essentials •Querying Techniques for Relational Data Analysis •Advanced Querying for Enhanced Proficiency and Insights

Course 09: Introduction to Computer Vision

This course will introduce you to the world of computer vision, demonstrate an understanding of image processing and different methods to extract informative features from images, build convolutional neural networks (CNNs) to unearth hidden patterns in image data, and leverage common CNN architectures to solve image classification problems. •Image Processing •Convolutional Neural Networks

Course 10: Advanced Agentic AI

This course explores advanced concepts in Agentic AI, beginning with advanced reasoning and agent protocols, and multi-agent systems, where learners delve into the interaction and coordination between multiple AI agents to solve complex problems collaboratively. The module also addresses the crucial aspect of securing agentic AI solutions, emphasizing the importance of implementing robust security measures to protect AI systems from vulnerabilities and ensure safe deployment in real-world applications. •Advanced Reasoning and AI Agent Protocols •Multi-Agent Systems •Securing Agentic AI Solutions

Course 11: MLOps and LLMOps for Scalable Deployment

This course provides a guide to the lifecycle of machine learning and generative AI applications, bridging the gap between development and production. You will explore the fundamentals of DevOps and MLOps by implementing robust version control and automating workflows with GitHub Actions, transition into building CI/CD pipelines, where you will leverage MLflow for experiment tracking and end-to-end model lifecycle management, and explore the specialized field of LLMOps, gaining the technical expertise to architect scalable serving infrastructure, optimize inference, and manage the unique deployment challenges of Generative AI solutions. •Introduction to DevOps and MLOps •Building CI/CD Pipelines •LLMOps for GenAI Solutions

Course 12: Capstone

This course will help you identify and define a real-world problem, considering factors such as data availability, feasibility, and potential impact, design and develop an AI solution that addresses the identified problem, explore, analyze, and process the data, apply and evaluate appropriate AI techniques to implement the solution effectively, and communicate insights and implications to stakeholders.

Additional Modules: Learn at your Own Pace

•Course 1: Multimodal Generative AI (Masterclass only) •Course 2: Fine-tuning LLMs •Course 3: Object Detection and Segmentation •Course 4: Recommendation Systems •Course 5: Reinforcement Learning •Course 6: Applied Statistics •Course 7: Model Interpretability •Course 8: Time Series Forecasting

Year 2 (Deakin Masters)

TRIMESTER 1

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

TRIMESTER 2

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

TRIMESTER 3

Human Aligned Artificial Intelligence Key Highlights: • Understand the need for aligning AI with human requirements, including ethics, safety, and explainability • Explore concepts such as artificial general intelligence, superintelligence, consciousness, and ethical decision-making • Study methods including safe exploration, constrained AI, interpretability, transparency, and interactivity • Analyse emerging areas such as AI pedagogy, industry standards, and black, grey, and white box systems, with an emphasis on ongoing research beyond the unit content Mathematics for Artificial Intelligence Key Highlights: • Explain the role and application of mathematical concepts associated with artificial intelligence • Identify and summarise key mathematical concepts and techniques required to solve AI-related problems • Verify and critically evaluate results, and communicate findings to a range of audiences • Read and interpret mathematical notation and clearly communicate problem-solving approaches

Meet your faculty

  • Gang Li - Faculty Director

    Gang Li

    Professor; Faculty of Science Engineering and Built Environment/School of Information Technology

    Professor at Deakin and Director across multiple AI labs

    IEEE Senior Member with expertise in AI, data privacy and ML

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  • Dr. Sutharshan Rajasegarar - Faculty Director

    Dr. Sutharshan Rajasegarar

    Associate Professor; Faculty of Science Engineering and Built Environment/School of Information Technology

    Course Director for Data Science at Deakin University

    Research leader in AI and federated machine learning

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  • Dr. Asef Nazari - Faculty Director

    Dr. Asef Nazari

    Associate Professor; Faculty of Science Engineering and Built Environment/School of Information Technology

    Associate Professor at Deakin and HDR Coordinator

    Expert in optimisation, AI and large-scale data systems

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  • Dr. Bahareh Nakisa - Faculty Director

    Dr. Bahareh Nakisa

    Senior Lecturer Faculty of Science Engineering and Built Environment/School of Information Technology

    Senior Lecturer in Applied AI and Course Director at Deakin

    Expert in AI, deep learning and human-centred AI systems

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  • Fatima Ansarizadeh - Faculty Director

    Fatima Ansarizadeh

    Lecturer, Applied Artificial Intelligence; Faculty of Science Engineering and Built Environment/School of Information Technology

    Applied AI Lecturer at Deakin with a PhD from Swinburne

    Leads applied research in AI, ML and Data Science domains

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  • Dr Wei-Yu Chiu - Faculty Director

    Dr Wei-Yu Chiu

    Associate Professor, Mathematics; Faculty of Science Engineering and Built Environment/School of Information Technology

    Associate Professor at Deakin, specialising in AI for energy systems

    Expert in ML, RL and optimisation for smart energy solutions

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  • Dr Kelvin Li - Faculty Director

    Dr Kelvin Li

    Lecturer, Mobile and Quantum Computing; Faculty of Science Engineering and Built Environment/School of Information Technology

    Lecturer at Deakin in Quantum Computing and Cryptography

    Expert in lattice cryptography and privacy-preserving AI

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  • Dr Thommen George - Faculty Director

    Dr Thommen George

    Lecturer, Information Technology (AI); Faculty of Science Engineering and Built Environment/School of Information Technology

    Lecturer in AI at Deakin and Associate Director, Bachelor of AI

    Expert in reinforcement learning and human-guided AI systems

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  • Dr Anuroop Gaddam - Faculty Director

    Dr Anuroop Gaddam

    Senior Lecturer; Faculty of Science Engineering and Built Environment/School of Information Technology

    Senior Lecturer at Deakin, specialising in AI, ML and IoT

    Expert in health informatics and smart, sustainable systems

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  • Dr. Kumar Muthuraman - Faculty Director

    Dr. Kumar Muthuraman

    Faculty Director, McCombs School of Business, The University of Texas at Austin

    Faculty Director, Center for Analytics and Transformative Technologies

    21+ years' experience in AI, ML, Deep Learning, and NLP.

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  • Dr. Abhinanda Sarkar - Faculty Director

    Dr. Abhinanda Sarkar

    Senior Faculty & Director Academics, Great Learning

    30+ years of experience in data science, ML, and analytics.

    Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.

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  • Dr. D Narayana - Faculty Director

    Dr. D Narayana

    Senior Faculty, Academics, Great Learning

    18+ years in AI, ML, and financial engineering solutions

    PhD in Mathematics from Pierre and Marie Curie University, France

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  • Dr. Pavankumar Gurazada - Faculty Director

    Dr. Pavankumar Gurazada

    Senior Faculty, Academics, Great Learning

    15+ years of experience in marketing, digital marketing, and machine learning.

    Ph.D. from IIM Lucknow; MBA from IIM Bangalore; IIT Bombay graduate.

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Course Fees

Invest in your career

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    A global masters degree & PG certificates

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    Practical, hands-on learning from world-class faculty

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    Industry-Focussed Curriculum

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    Dedicated career support

Take the next step

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Application closes: 15th Apr 2026

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Application closes: 15th Apr 2026

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Admission Process

Admissions close once the required number of participants enroll. Apply early to secure your spot

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    Apply

    Fill out an online application form

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    Get Reviewed

    Go through a screening call with the Admission Director’s office

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    Join the program

    Your profile will be shared with the Program Director for final selection

Course Eligibility

  • Applicant must meet Deakin’s minimum English Language requirement
  • Candidates should have a bachelors degree (minimum 3-year degree program) in a related discipline OR a bachelors degree in any discipline with at least 2 years of work experience

Limited seats available

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Master of Applied Artificial Intelligence (Global)

Download Brochure
Apply Now

phone icon Application closes 15th Apr 2026

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