Course Catalog 2009-2010 Postgraduate Basic Pori International Postgraduate Open University |Degrees|     |Study blocks|     |Courses|

Person responsible

Ari Visa

Implementations

No implementations

Requirements

Final exam, attendance at the classroom exercises and assignment.
Completion parts must belong to the same implementation

Principles and baselines related to teaching and learning

-

Learning outcomes

Learning outcomes: The student can list the mentioned learning rules. The student can describe them and is capable to apply them to train neural networks. The student is capable to list to analyse the lectured neural networks (MLP,SVM,SOM and recurrent networks). The student is capable to analyse the own problem and to select the most suitable, lectured neural network. The student has a certain capability to create new solutions based on the lectured material.

Content

Content	Core content	Complementary knowledge	Specialist knowledge
1.	Learning processes
2.	Learning machines with a teacher	Multilayer Perceptrons Radial-Basis Function Networks Support Vector Machines Committee Machines
3.	Learning machines without a teacher	Principal Component Analysis with Neural Networks Self-Organizig Maps Boltzmann Machine
4.	Nonlinear dynamical systems	Temporal Processing Using Feed Forward Network Dynamically Driven Recurrent Network

Evaluation criteria for the course

Exam and approx. 70% attendance at the exercises.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Partial passing:

Completion parts must belong to the same implementation

Study material

Type	Name	Author	ISBN	URL	Edition, availability, ...	Examination material	Language
Book	"Neural Networks: a Comprehensive Foundation"	Haykin, S.			2nd edition, Prentice-Hall Inc, 1999		English

Prerequisites

Correspondence of content

Additional information

Lectures in English or in Finnish.

Last modified	13.01.2010
Modifier	Ari Visa