CISC 473/3.0 Deep Learning
Original Author: David Skillicorn
Last Revised: 2019-03-20
Design of deep neural networks based on leading-edge algorithms such as Restricted Boltzmann Machines, Recurrent Neural Networks, Convolutional Neural Networks, Long-Short Term Machines. Autoencoding as a clustering technique. Applications to prediction problems in natural language and images.
Learning hours: 120 (36L; 84P)
This course is required for the
focus of the COMP degree plan.
- Introduction to deep networks, review of optimisation for learning, backpropagation (1 week)
- Restricted Boltzmann machines (RBMs), stacking RBMs (1 week)
- Autoencoding as a clustering technique, Stacked Denoising Autoencoders (2 weeks)
- Vanishing gradient problem, extending backpropagation to other network structures (1 week)
- Recurrent neural networks, applications to natural language prediction problems (2 weeks)
- Convolutional neural networks, applications to natural language and image prediction problems (2 weeks)
- Long-short term machines (LSTMs), biLSTMs, applications to natural language problems (2 weeks)
- Matching techniques to problem domains, comparative performance (1 week)