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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.
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Employable, innovative and enterprising
Students will develop key skills and attributes sought by employers that can be used in a range of applications.
Engaged with the community
Students will have observed and understood a culture within a community by reflecting on their own performance and experiences within that community.
Subject to HoS approval
DATA473
Students must attend one activity from each section.
Computer labs will start on Feb 26th.
Katharina Dost
In-person attendance will be required for "presentation of research paper" and the exam
Library portalPostgraduate Learn
Domestic fee $1,247.00
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 .