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|
|Teaching period||Semester 1 (On campus)|
|Domestic Tuition Fees (NZD)||$1,348.60|
|International Tuition Fees (NZD)||$5,967.53|
- NEUR 420
There are no formal prerequisites for the 400-level papers, but prior knowledge is assumed.
Computer Science Adviser, firstname.lastname@example.org
- More information link
- View more information for COSC 420
- 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
- 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,
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