DATA473-21S1 (C) Semester One 2021

Special Topic: Foundations of Deep Learning

15 points

Details:
Start Date: Monday, 22 February 2021
End Date: Sunday, 27 June 2021
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 7 March 2021
  • Without academic penalty (including no fee refund): Friday, 14 May 2021

Description

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

Pre-requisites

Subject to the approval of the Head of School

Timetable 2021

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 11:00 - 12:00 Jack Erskine 101
22 Feb - 4 Apr
3 May - 6 Jun
Lecture B
Activity Day Time Location Weeks
01 Tuesday 10:00 - 11:00 Jack Erskine 101
22 Feb - 4 Apr
26 Apr - 6 Jun
Computer Lab A
Activity Day Time Location Weeks
01 Tuesday 15:00 - 16:00 Rehua 008 Computer Lab
22 Feb - 4 Apr
26 Apr - 6 Jun

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.

Lecturer

Varvara Vetrova

Contact Person

Varvara Vetrova

Assessment

Assessment Due Date Percentage 
Assignments (x3) 60%
Final examination 40%


At least 40% should be obtained in the final exam to pass the course.

Indicative Fees

Domestic fee $952.00

* 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 DATA473 Occurrences

  • DATA473-21S1 (C) Semester One 2021