Course syllabus
Artificiella neuronnätverk
Artificial Neural Networks
EXTP80, 7,5 credits, A (Second Cycle)
Valid for: 2016/17
Decided by: Education Board B
Date of Decision: 2016-03-29
General Information
Language of instruction: The course will be given in English on demand
Aim
The aim of the course is to give the student knowledge about
artificial neural networks, both theoretical knowledge and how to
use them in practical applications such as pattern recognition,
function approximation and optimization problems.
Learning outcomes
Knowledge and understanding
For a passing grade the student must
- understand and be able to use the simple perceptron for
linear problems.
- understand and be able to use the multilayer perceptron,
including methods for learning, choices of error function, model
selection and generalization.
- understand the meaning of feedback networks and its usage
within time series analysis.
- understand and be able to use various types of feedback
networks, such as the FIR-network, networks with time lags,
multilayer perceptrons with feedback and networks with context
units.
- understand and be able to use networks for principal
component analysis, networks for clustering and networks for
supervised learning vector quantization (LVQ).
- understand and be able to use Self-organizing feature maps
(SOFM).
- understand and be able to formulate simple combinatorial
optimization problems and use feedback networks to find approximate
solutions to such problems.
- understand and be able to use the mean field approximation in
connection with networks for combinatorial optimization.
Competences and skills
For a passing grade the student must
- be able to describe and handle network ensembles and describe
Bayesian training of multilayer perceptrons.
- be able to describe and handle fully connected feedback
networks for associative memories (the Hopfield model) and the
simulated annealing optimization technique.
- be able to write a computer program that trains a multilayer
perceptron for a binary classification problem and be able to
evaluate the performance of the network.
- bea ble to show why an ensemble of networks often performs
better compared to a single network.
- be able to write a computer program that uses the Hopfield
model to find approximate
solutions to the graph bisection problem.
Contents
- Feedforward networks
- Feedback networks
- Self-organizing networks
- Networks for combinatorial optimization
Examination details
Grading scale: TH
Assessment: To pass the entire course, a passed test, passed reports on the computer exercises and participation in all compulsory course elements is required.
Admission
Admission requirements:
The number of participants is limited to: No
Reading list
- As posted on our webpage and billboard.
Contact and other information
Course coordinator: Mattias Ohlsson, mattias@thep.lu.se
Course homepage: http://cbbp.thep.lu.se/~mattias/teaching/fytn06/