Numerical methods and machine learning

This unit conveys the fundamentals of numerical analysis techniques and their application to data analysis and the solution of engineering problems. You will be introduced to programming structures, use of AI in programing, software documentation, version control and data management, profiling and hardware optimisation, conventions of scientific computing, numerical errors and stability, techniques for interpolation, integration, the solution of ordinary and partial differential equations, and principles of machine learning.

Active learning - You will actively engage in applying your knowledge, skills, and attributes in interactive, collaborative, and reflective activities.

Problem-based learning - This unit includes problem-based learning approaches, where you engage in research, integrate theory and practice and apply knowledge and skills to develop viable solutions in response to a problem or set of problems.

The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.