DATA473-20S1 (C) Semester One 2020

Special Topic: Foundations of Deep Learning

15 points

Start Date: Monday, 17 February 2020
End Date: Sunday, 21 June 2020
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 28 February 2020
  • Without academic penalty (including no fee refund): Friday, 29 May 2020


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.

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


Subject to the approval of the Head of School

Timetable 2020

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Wednesday 09:00 - 10:00 Jack Erskine 505 (19/2-18/3)
- (25/3, 22/4-27/5)
17 Feb - 29 Mar
20 Apr - 31 May
Lecture B
Activity Day Time Location Weeks
01 Friday 09:00 - 10:00 Jack Erskine 505 (21/2-20/3)
- (24/4-29/5)
17 Feb - 22 Mar
20 Apr - 31 May
Computer Lab A
Activity Day Time Location Weeks
01 Monday 13:00 - 14:00 Jack Erskine 436 Computer Lab (17/2-16/3)
- (23/3, 20/4, 4/5-25/5)
17 Feb - 29 Mar
20 Apr - 26 Apr
4 May - 31 May

Timetable Note

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.


Varvara Vetrova and Rachael Tappenden

Contact Person

Varvara Vetrova

Additional Course Outline Information

Assessment and grading system

Assessment will be based on the following components of the course:
Assignments 60%
Final examination 40%

Indicative Fees

Domestic fee $942.00

* 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.

All DATA473 Occurrences

  • DATA473-20S1 (C) Semester One 2020
  • DATA473-20S2 (C) Semester Two 2020 - Not Offered