FYTN14, Introduction to Artificial Neural Networks and Deep Learning, 7.5 HP

Course responsible person: Mattias Ohlsson

Next time:
Fall 2017, course starts October 30.

There is a new schedule now! Due to a large number of (expected) students on the course a change of lecture hall was needed. The general structure is still Mondays 13-15, Tuesdays 10-12 and Thursdays 10-12, with a few exceptions. The new exam date is Wednesday January 10, 14:00-19:00. New schedule at TimeEdit

Course content

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)

Deep learning
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 Jan, 2007.
Available at http://home.thep.lu.se/~mattias/teaching/fytn14/index.html.
Latest update: October 02, 2017.

©2007-2017 Mattias Ohlsson