Masters in Artificial Intelligence (Applied AI) from Deakin University

The curriculum is structured as a 24-month integrated pathway, transitioning from foundational concepts to advanced, specialized AI applications. For a detailed breakdown of modules and learning outcomes, learners are advised to refer to the official program brochure.

Learners are taught by faculty from Deakin University for the Masters degree, and by faculty from the McCombs School of Business, University of Texas at Austin for the PGP-AIML. Below is the complete list of faculty members associated with the program:
Deakin University Faculty

• Dr. Sutharshan Rajasegarar: Associate Professor, Director of Data Analytics Research Lab, and Course Director of Data Science.
• Dr. Wei-Yu Chiu: Associate Professor in Mathematics within the Faculty of Science, Engineering and Built Environment.
• Fatima Ansarizadeh: Lecturer in Applied Artificial Intelligence.
• Dr. Gang Li: Professor and IEEE Senior Member.
• Dr. Asef Anzari: Senior Lecturer in Mathematics and Academic Director of Research Training.
• Dr. Thommen George: Lecturer in Information Technology (AI).
• Dr. Anuroop Gaddam: Senior Lecturer in IT Management.
• Dr. Bahareh Nakisa: Senior Lecturer in Applied Artificial Intelligence.
• Dr. Kelvin Li: Lecturer in Mobile and Quantum Computing.

PGP AIML Faculty

• Dr. Kumar Muthuraman - Faculty Director, Center for Research and Analytics at UT Austin, Texas
• Dr. Pavankumar Gurazada - Senior Faculty, Academics at Great Learning
• Dr. D Narayana - Professor of Artificial Intelligence and Machine Learning at Great Learning
• Prof. Mukesh Rao - Director at Great Learning
• Dr. Abhinanda Sarkar - Consultant Data Scientist at Compegence
• Prof. Dan Mitchell - Clinical Assistant Professor at UT Austin

Yes. The program emphasises practical learning through hands-on projects and a final Capstone project in the PGP-AIML phase to ensure learners build industry-relevant skills.

The curriculum covers 27+ languages and tools. These include:

• Programming & Data Science - Python, pandas, NumPy, seaborn, matplotlib, and statsmodels
• Machine Learning & Deep Learning - scikit-learn and TensorFlow
• Generative AI & LLMs - LangChain, LangGraph, LangSmith, OpenAI API, ChatGPT, Gemini, Hugging Face, DSPy, and NotebookLM
• Infrastructure & Databases - Chroma DB, MCP, and n8n

To qualify for our Masters program in AI, learners must meet the following criteria:

• Programming & Data Science - Python, pandas, NumPy, seaborn, matplotlib, and statsmodels
• Machine Learning & Deep Learning - scikit-learn and TensorFlow
• Generative AI & LLMs - LangChain, LangGraph, LangSmith, OpenAI API, ChatGPT, Gemini, Hugging Face, DSPy, and NotebookLM
• Infrastructure & Databases - Chroma DB, MCP, and n8n

Yes, non-programmers can apply for our Master of Applied Artificial Intelligence (Global) program.

Due to limited seats, admission is on a first-come, first-served basis. Interested candidates should submit an Expression of Interest via email. Please contact your Program Advisor for more information regarding the application process.

The fee for a Masters in Artificial Intelligence (AI) varies widely based on country, university, and format. The total tuition fee for our Masters program in AI is one-tenth the cost of a typical two-year on-campus AI and Machine Learning Masters program. For the most up-to-date information on the course fee, please refer to the official program page here.

Learners can pay the program fees using net banking, credit cards and debit cards.

Graduates earn a Master of Applied Artificial Intelligence (Global) from Deakin University, along with industry-relevant technical skills across machine learning, deep learning, generative AI, and AI solution engineering.

• Develop strong technical expertise in machine learning, deep learning, generative AI, and AI solution engineering
• Build practical skills through hands-on projects and real-world applications
• Gain exposure to industry-relevant tools, frameworks, and problem-solving approaches
• Apply AI techniques across domains for data-driven decision-making and automation Overall, the program is designed to prepare learners for evolving opportunities in the AI ecosystem and support long-term career growth in a rapidly advancing field.

This program equips you with the technical expertise and business acumen needed for various high-impact roles. Based on the specialized tools and curriculum covered, such as MLOps, Deep Learning, and Computer Vision, you can pursue the following positions:

• AI Engineer - Develops, manages, and oversees AI initiatives within an organization
• Machine Learning Engineer - Designs and implements machine learning algorithms and predictive models
• Data Scientist - Analyzes complex data to drive data-informed decisions
• AI Solutions Architect - Designs end-to-end AI systems and oversees their operationalization
• MLOps Engineer - Builds and maintains ML pipelines, bridging model development and production
• Robotics Engineer - Develops intelligent systems that sense and interact with their environment
• Computer Vision Engineer - Works on systems that process and analyze visual data, including image processing and CNNs
• NLP Specialist - Focuses on language models, speech processing, and AI assistant development

While specific salary figures vary by region, industry, and experience, a Masters degree from Deakin University, a globally reputed Australian institution ranked #207 by QS 2026, can strengthen your professional credentials and earning potential in the AI field.