Course Catalog 2008-2009 Basic Basic Pori International Postgraduate Open University |Degrees|     |Study blocks|     |Courses|

Course´s person responsible

Ari Visa

Implementations

	Lecture times and places	Target group recommended to
Implementation 1	Per 3 : Monday 10 - 12, TB222 Friday 10 - 12, TB223 Wednesday 14 - 16, TB222

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

-

Objectives

To give basic knowledge of neuro computing and to apply neuro computing to some application fields.

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.

More precise information per implementation

	Description	Methods of instruction	Implementation
Implementation 1			Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 %

Last modified	26.06.2008
Modifier	Kirsi Järnström