FYTN14, Introduction to Artificial Neural Networks and Deep Learning, 7.5 HP
Course responsible person: Mattias Ohlsson
Fall 2018, course starts November 5.
Schedule will come soon!
The course covers the most common models in artificial neural networks with a focus
on the multi-layer perceptron. The course also provides an introduction to deep
learning. Selected topics:
Feed-forward neural networks
The simple perceptron and the multi-layer
perceptron, choice of suitable error functions and techniques to minimize them,
how to detect and avoid overtraining, ensembles of neural networks and
techniques to create them, Bayesian training of multi-layer perceptrons
Recurrent neural networks
Simple recurrent networks and their use in time series
analysis, fully recurrent for both time series analysis and associative memories
(Hopfield model), the simulated annealing optimization technique
Self-organizing neural networks
Networks that can extract principal components,
networks for data clustering, learning vector quantization (LVQ), self-organizing
feature maps (SOFM)
Overview of deep learning, convolutional neural networks for
classification of images, different techniques to avoid overtraining in deep
networks, techniques to pre-train deep networks
This page was created in
Available at http://home.thep.lu.se/~mattias/teaching/fytn14/index.html.
Latest update: July 18, 2018.