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Special Topic: Foundations of Deep Learning
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 practise. Demonstrate knowledge in designing and analysis of deep neural network modelsBe able to undertake a research project involving deep neural networks
Subject to the approval of the Head of School
Students must attend one activity from each section.
Please see the Course Information Page for lecture and lab times, and their locations. Please note these can be subject to change and it is recommended that you check these times in the first couple of weeks of term. There are two lectures and a one hour lab each week.
and Rachael Tappenden
Assessment will be based on the following components of the course:Assignments 60%Final examination 40%
Domestic fee $942.00
International Postgraduate fees
* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.
For further information see
Mathematics and Statistics.