Skip to content

Online Master of Artificial Intelligence: Course structure

Curriculum Details

12–16 subjects required

You can complete our online Master of Artificial Intelligence course in just 14 months with 12 subjects if you choose to study full-time and have an undergraduate degree in a related field. Many students graduate within two and a half years, but if your schedule becomes challenging, you may take up to five years to complete the course.

If your undergraduate degree is in an unrelated field, you’ll learn everything you need to know with four introductory IT fundamentals subjects. You’ll graduate with 16 subjects so you can work in this growing sector.

For more information about the duration or the course structure, speak with an enrolment advisor on (+61 3) 9917 3009 or request more information now.



The Academic Integrity Module will introduce you to academic integrity standards, so you’re informed about how to avoid plagiarism and academic misconduct.  You’ll complete four parts that cover academic misconduct and academic integrity decisions, such as cheating, plagiarism and collusion.  You’ll learn about the text-matching tool, Turnitin, that is used at La Trobe, how to get help and where to go to develop referencing skills.

In this subject, you will learn computer system organization and its associated topics. It covers the hardware components of the computer, data storage and retrieval, and introduces system software, computer networks, data communications, the Internet, operating systems, file management systems and security. You will also be introduced to information systems and application software packages.

In this subject, you will be introduced to the steps involved in designing and creating software solutions for a range of practical problems. To enable you to design and implement solutions, you will be introduced to methods for analysis of requirements, development of the overall structure of a solution, and identification of its key parts, and on this basis, to incrementally build and test the solution. To develop your problem-solving skills, problems drawn from different domains, with increasing complexity, will be presented for your practice. You will be introduced to the concepts of class and object, to represent real-world objects to solve problems arising from an application domain. Python is used as the programming language in the subject. The strengths of Python, in particular its supports for quick testing of ideas, are exploited to facilitate the development of your problem-solving skills and effective software development practice.

This subject develops an understanding of probability and statistics applied to Data Science. Probability topics include joint and conditional probability, Bayes’ Theorem and distributions such as the uniform, binomial, Poisson and normal distributions as well as properties of random variables and the Central Limit Theorem. Statistical inference and data analysis is also considered covering, among other topics, significance testing and confidence intervals with an introduction to methods such as ANOVA, linear and nonlinear regression and model verification. Applications to data science are considered and students will be exposed to the R statistical package as well as the mathematical type-setting package LaTeX.

Quantitative analysis plays an important role in industrial data analytics and knowledge engineering, which makes it very useful to develop computing skills for data regression and classification. This subject covers fundamentals of machine learning techniques in theory and practice. The subject is designed to focus on solving industrial data modelling problems using neural networks. You will learn how to test various learning algorithms and compare performance evaluations. Some advanced machine learning techniques for data classification will also be addressed. You will work with industrial data modelling in labs and assignments to consolidate your knowledge and gain hands-on experience with machine learning applications.

Artificial Intelligence (AI) is the field of engaging computers for reasoning and decision-making. In this subject, you will be introduced to fundamental concepts and different application fields of AI. Main topics include searching, knowledge representation and reasoning, expert system design and development, responsible AI principles and applications. Practice on design and development of AI models for real world problems will be offered in labs.

This subject starts with an overview of the architecture and management of database systems, and a discussion of different existing database models. The main focus includes relational database analysis, design, and implementation. The students learn: relational algebra as the formal foundation of relational databases; relational conceptual design using an entity-relationship diagram; relational logical database design; security and integrity; and SQL implementation of relational database queries. Students will also learn advanced normalization theory and the techniques to remove data anomalies and redundancies. In this subject, students are required to design a database application that meets the needs of a system requirement specification, and to implement the system using a commercial standard database system such as ORACLE or POSTGRESQL. In addition, a selection of advanced topics in databases will be introduced and discussed.

This subject provides necessary skills and techniques to manage large-scale information technology projects, with strong focus on the analytical side of project management, referring to scheduling, cost, and resource management, as well as the ‘people’ and client management issues that must be dealt with in order to ensure successful projects. Students learn to design Information Technology projects covering network management or software development or data science for efficiency, portability and re-use, as well as to take advantage of different standards and system utilities, data and information management techniques.

Deep learning is currently the central machine learning method fuelling the artificial intelligence revolution. In this subject you learn how to apply deep learning algorithms to solve real-world problems. This subject does not assume you have previous machine learning experience, therefore it starts teaching deep learning at a very introductory level. You learn how deep learning techniques can be applied to such tasks as image recognition, sentiment classification, machine translation, question and answering, speech synthesis, etc. The practical skills taught in this subject will allow you to build production level deep learning software that can scale out to millions of users. You will be introduced to the popular deep learning programming frameworks of Pytorch and Tensorflow and advanced deep learning techniques such as reinforcement learning, generative adversarial networks and few shot learning.

The purpose of this subject is to outline the basic principles of Entrepreneurship. It will examine the steps required in developing an idea into a business and will explore the tools and necessary insights to make a successful venture. The subject will involve theory, case studies and guest speakers on start-up issues, pitfalls, and ingredients for success. Students will also develop professional skills related to ethical and moral decision making and evaluate the social implications of their work and the broader global context. The subject requires active participation in group discussions and activities.

Core choice specialisation: Natural language processing Select 60 credit points


Natural Language Processing (NLP) is broadly concerned with the interactions between computers and natural (i.e., human) languages; more particularly, it is concerned with the question of how to program computers to process and analyse large amounts of natural language data. Following a review of the essential mathematical and linguistic concepts underlying natural language processing, you will develop skills in important natural language processing sub-tasks including accessing corpora, tokenisation, morphological analysis, word sense disambiguation, part-of speech tagging, and analysing sentence structure. You will then apply these skills in the context of applications such as text categorisation, text clustering, text recommendation, and information retrieval. Where appropriate, both lexical (i.e. dictionary-based) and machine learning approaches will be used.

This subject introduces the Python programming language and its advanced features to effectively deal with real-world applications. You will establish an understanding of fundamental and advanced topics in Python language and gain experience in program design and implementation of algorithms to solve real-world problems. Topics covered include control structures, built-in and complex data types, basic and advanced data structures, modular program structure, iteration and recursion, file input and output, iterative and generative, object-oriented programming, exception handling, Python for data science, Python for machine learning, Python for artificial intelligence and Python packages. One or more applications associated with each topic will be discussed. You will learn and implement advanced Python concepts and packages.

Data Mining refers to various techniques which can be used to uncover hidden information from a database. The data to be mined may be complex data including big data, multimedia, spatial and temporal data, biological and health data. Data Mining has evolved from several areas including: databases, artificial intelligence, algorithms, information retrieval and statistics. This subject is designed to provide you with a solid understanding of data mining concepts and tools. The subject covers algorithms and techniques for data pre-processing, data classification, association rule mining, and data clustering. The subject also covers domain applications where data mining techniques are used.

In this subject you will be provided with specialist knowledge and tools required to formulate solutions to complex data p problems encountered by data scientists. You will learn various data exploration techniques and analysis tools. Selected topics include data cleaning, data normalisation, data visualisation and data exploration. One or more applications associated with each problem will also be discussed. You will learn the fundamentals of exploratory data analysis techniques, statistical learning, and correlation analysis to solve these problems. You will also learn to implement data exploration methods and analysis tools using the R programming language.

Students undertake research, across both CSE5001 and CSE5TSB, that takes the equivalent of eight or nine months of continuous work under the supervision of a member of staff. In the first semester, a literature review is written up and submitted as a hurdle requirement for the subject. A list of prospective thesis topics is available from the Department of Computer Science and Information Technology.

Core choice: Pathway Select one pathway worth 30 credit points from the list below


The project focuses on developing students’ skills in teamwork, system design, implementation, testing and documentation. Students learn to design Information Technology projects covering network management or software development or data science for efficiency, portability and re-use, as well as to take advantage of different standards and system utilities, data and information management techniques. The projects require students to work in small development teams and result in the development of a small-scale industry-based system. The laboratory work is designed to bring students up to speed on relevant development skills and to provide them with a working knowledge sufficient for industrial-type network, data science and software development projects. The subject also integrates previously learned project management skills and knowledge relating to social and ethical issues.

The project focuses on developing students’ skills in system design, implementation, testing and documentation for solving Research problems in Information Technology. Students learn to understand the underlying research question specific to the chosen project; design, develop, test and document a software(or simulation) system for the analysis of the research problem.

Students undertake a research, across both CSE5001 and CSE5TSB that takes the equivalent of eight or nine months of continuous work under the supervision of a member of staff. In the second semester, a minor thesis is written up and submitted as a hurdle requirement for the subject. The student also required to deliver a presentation based on the research at the end of the semester.