Modelling and Analysis for AI (ELEN90097)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

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Semester 1
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This subject builds up the fundamentals for modelling dynamical systems, with a key focus on the aspects and decisions of modelling that are relevant for the application of AI and data-intensive learning methods. The discussion and evaluation of modelling methods focuses on how model fidelity influences simulation-to-real transfer; how modelling and simulation decisions influence computation time required for training and validation; and how discrete-time models introduce complexity when representing continuous-time engineering systems. Subsequently, it introduces the basic principles and engineering applications of programming and data structures in a condensed form with a project-centric pedagogy.  It covers the fundamentals of databases and data structures, basic algorithms, scientific programming, and classic AI problem solving. It will focus specifically on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how programming and databases play a role in modern engineering and cyber-physical systems.

INDICATIVE CONTENT
Topics covered may include:

  • Models for engineering systems in multiple disciplines, including analysis of what makes the models amenable to AI and data-intensive learning methods.
  • Principles for simulating dynamic systems that are most relevant for the use of AI methods and to address these principles with existing software tools.
  •  Scientific programming for modelling using Python programming language and libraries such as scipy and numpy.
  • Engineering data structures and time series data and their storage in SQL and noSQL databases.

Example engineering applications will be taught via projects in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks.

Intended learning outcomes

On completion of this subject, students should be able to:

  • Employ and explain modern software tools used for simulation of dynamical systems, including continuous, discrete, and hybrid systems.
  • Apply the axioms of probability, independence, random variables and vectors, conditioning, and Bayes' rule to solve engineering problems.
  • Apply important distributions and informatic theoretic tools for analysing distributions, such as: entropy; KL divergence; mutual information to evaluate alternatives and synthesise solutions to engineering problems.
  • Analyse real-world problems and utilise modern programming languages to synthesise solutions, recognising the need to formulate algorithms that are amenable to processing by a computer such as recursion, sorting and determining shortest-path trees.
  • Communicate effectively with professionals across different engineering disciplines, through media such as concise technical reports and informational videos or live presentations.

Generic skills

On completion of this subject, it is expected that the student will have developed the following generic skills:

  • Ability to apply knowledge of basic science and engineering fundamentals
  • Ability to undertake problem identification, formulation and solution
  • Ability to utilise a systems approach to design and operational performance
  • Capacity for independent critical thought, rational inquiry and self-directed learning
  • Ability to communicate effectively, with the engineering team and with the community at large

Last updated: 4 March 2025