Accessibility Skip to Global Navigation Skip to Local Navigation Skip to Content Skip to Search Skip to Site Map Menu

COSC420 Neural Networks

Due to COVID-19 restrictions, a selection of on-campus papers will be made available via distance and online learning for eligible students.
Find out which papers are available and how to apply on our COVID-19 website

Introduction to neural networks - computational tools inspired by the brain - which give a different perspective on the nature of computation and complex topics such as vision, language, learning and memory.

Despite its slow "hardware," the brain is a much more powerful and sophisticated computational system than any computer ever built. What can brain-inspired computation teach us about complex tasks, such as natural language processing, vision and control and optimisation problems? Artificial neural networks are a family of methods that try to address these issues and explore "brain-like" computation, information processing and learning. "Deep learning" is currently a popular technology based on neural networks and used by organisations such as Google, Baidu, Microsoft and Apple.

Paper title Neural Networks
Paper code COSC420
Subject Computer Science
EFTS 0.1667
Points 20 points
Teaching period Semester 1 (On campus)
Domestic Tuition Fees (NZD) $1,348.60
International Tuition Fees (NZD) $5,967.53

^ Top of page

NEUR 420

There are no formal prerequisites for the 400-level papers, but prior knowledge is assumed.


Computer Science Adviser,

Teaching staff

Lecturer: Lech Szymanski

Paper Structure

The paper introduces the neural networks in a lecture-driven format, with some elements of discussion based on prescribed reading of relevant literature. We will cover an introduction to the topic, the practical use and tuning of neural nets (using TensorFlow) and fundamental algorithms and architectures, including (a selection of)

  • Multi-layer Perceptron networks
  • Backpropagation
  • Hopfield nets
  • Boltzmann machines
  • Unsupervised learning
  • Deep learning
  • Convolutional networks
  • Reinforcement learning
  • Sequence-to-sequence learning
Teaching Arrangements

One session per week. Sessions include a mixture of lecture-type presentations of new material and tutorial-type discussions.


Textbooks are not required for this paper.

Course outline
View the course outline for COSC 420
Graduate Attributes Emphasised
Interdisciplinary perspective, Lifelong learning, Communication, Critical thinking, Research.
View more information about Otago's graduate attributes.
Learning Outcomes

This paper will enable students to:

  • Understand and implement a range of artificial neural networks
  • Understand the strengths and weaknesses of neural networks compared to traditional symbolic methods
  • Perform practical research with a neural network simulation and systematically present the outcomes
  • Perform a literature review and evaluate publications in the field

^ Top of page


Semester 1

Teaching method
This paper is taught On Campus
Learning management system


Stream Days Times Weeks
A1 Tuesday 09:00-10:50 9-13, 15-22