Artificial intelligence Training Courses and Workshops

On this course you will learn advanced techniques in Artificial Intelligence (AI), to create a range of artificial gameplaying agents and non-player characters for a wide variety of games, from chess through to complex first-person and real-time strategy and multiplayer games.

The course will cover advanced topics in machine learning, genetic and evolutionary algorithms, and neural networks. Options during the course will allow you to study areas such as computer graphics and animation, sound, software engineering and project management.

The course is designed for students with an enthusiasm for AI who prefer a hands-on course where your ideas can be implemented in a range of game environments, and for those with an enthusiasm for computer games aiming to get into AI design and development. You will be expected to have prior knowledge of programming skills and an enthusiasm for games, but knowledge of AI is not required.

Our graduates have skills that make them well suited to careers in the computer games industry, and to AI jobs in other areas such ... [Read More]

Topics in artificial intelligence: heuristic search, game playing, rule-based systems, genetic algorithms, and neural networks. Significant project work demonstrating various AI techniques

Fundamental concepts and general techniques in artificial intelligence. Main topics include goal tree search, logic and deduction, adduction, uncertainty, fuzzy logic, knowledge representations, machine learning, vision, and action planning. The LISP programming language is used extensively. Students are required to do programming assignments, complete a programming term project, and review case studies.

Artificial intelligence (AI) is the study of computer systems that appear to behave with some degree of human intelligence. This survey course covers major background and state-of-the-art topics in AI: game playing, natural language understanding, theorem proving, computer vision, genetic algorithms, and neural networks. Other topics in machine learning, planning, and expert systems are covered if time permits. The LISP programming language is introduced and used for some programming assignments; others may be completed in Java or C.

This unit introduces the fundamentals behind Artificial Intelligence (AI) and the different AI applications. It examines the various AI methods including: "A*" and other path finding systems, flocking, pattern, fuzzy logic, rule-base, probability, Bayesian techniques, neural networks and genetic algorithms.

Deakin University’s postgraduate Engineering program has been designed to strengthen, build on and extend your understanding of engineering principles required for successful engineering careers in Australia and overseas.

You will gain technical skills, a positive approach to problem solving and the ability to work in a team, enabling you to work on complex projects immediately.

You can choose from specialisms in Mechanical Engineering, Electronics Engineering or Civil Engineering. The specialisms all provide expertise in areas of high demand for graduates.

The program’s focus on practical experience and a supervised research project or industry project provides you with advanced project management skills. This experience equips you with the ability to apply your skills and knowledge to practical, industrially focussed problems.

This course focuses on design, development and implementation of effective systems for meeting information needs of management decision-makers. The course explains both model-based and data-based decision support systems and their use by managers in functional areas. Spreadsheets and applied artificial intelligence models, such as artificial neural network, and/or rule-based expert systems software may be used to introduce the decision-support process

This course focuses on concepts needed for analyzing, designing and applying effective systems for meeting management’s information needs for tactical short-term, and strategic long-term, planning and decision-making. The course covers various models of business intelligence and decision support systems, based largely on database design in support of data warehousing and data mining concepts. Topics include data modeling for the data warehouse and data mart, and application of OLAP and artificial intelligence concepts as applied to data mining

This course focuses on design, development and implementation of effective systems for meeting information needs of management decision-makers. The course explains both model-based and data-based decision support systems and their use by managers in functional areas. Spreadsheets and applied artificial intelligence models, such as artificial neural network, and/or rule-based expert systems software may be used to introduce the decision-support process

This course focuses on concepts needed for analyzing, designing and applying effective systems for meeting management’s information needs for tactical short-term, and strategic long-term, planning and decision-making. The course covers various models of business intelligence and decision support systems, based largely on database design in support of data warehousing and data mining concepts. Topics include data modeling for the data warehouse and data mart, and application of OLAP and artificial intelligence concepts as applied to data mining