DATA425-24S1 (C) Semester One 2024

Foundations of Deep Learning

15 points

Start Date: Monday, 19 February 2024
End Date: Sunday, 23 June 2024
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 3 March 2024
  • Without academic penalty (including no fee refund): Sunday, 12 May 2024


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.

Learning Outcomes

  • 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 models.
  • Be able to undertake a research project involving deep neural networks.
    • University Graduate Attributes

      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


Timetable 2024

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Wednesday 13:00 - 14:00 Jack Erskine 445
19 Feb - 31 Mar
22 Apr - 2 Jun
Lecture B
Activity Day Time Location Weeks
01 Thursday 13:00 - 14:00 Jack Erskine 340
19 Feb - 31 Mar
29 Apr - 2 Jun
Computer Lab A
Activity Day Time Location Weeks
01 Tuesday 11:00 - 12:00 Jack Erskine 442 Computer Lab
19 Feb - 31 Mar
22 Apr - 2 Jun

Course Coordinator

Varvara Vetrova


Assessment Due Date Percentage  Description
Theoretical foundations 20% Theoretical foundations
Reproducibility study 20% Reproducibility study
Review of research paper 20% Review of research paper
Exam 40% Exam

Indicative Fees

Domestic fee $1,110.00

* 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 .

All DATA425 Occurrences

  • DATA425-24S1 (C) Semester One 2024