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The aim of this course is to introduce students to foundational concepts of deep neural networks. The focus of this course is on both fundamental and applied methods in deep neural networks. A range of topics from convolutional and recurrent type networks to neural-network generative models and attention mechanisms will be introduced.
Understand concepts of mathematical foundations of deep learning such as empirical risk minimisation, convergence rates and capacity.Show competency in techniques used in deep neural network model optimisation and analysis. Demonstrate theoretical knowledge of principles governing success of deep learning methods in practiseDemonstrate knowledge in designing and analysis of deep neural network models.Be able to undertake a research project involving deep neural networks.
Subject to HoS approval
Students must attend one activity from each section.
Domestic fee $1,051.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Mathematics and Statistics