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

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 Friday 12 - 14, TB222 Tuesday 12 - 14, TB224 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

-

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.

More precise information per implementation

	Description	Methods of instruction	Implementation
Implementation 1	The course concentrates on basic and some more advanced methods of neuro computing.		Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 %

Last modified	13.01.2010
Modifier	Ari Visa