TIETS07 Neurocomputing 5 ECTS
Organised by
Degree Programme in Computer Sciences
Person in charge
Professor Martti Juhola
Preceding studies
Recommended:
Data Structures, basics of mathematics.
Corresponding course units in the curriculum
School of Information Sciences
Curricula 2015 – 2017
TIETS07 Neurocomputing 5 ECTS

Learning outcomes

After taking this course the student knows the basic methods of neurocomputing and their applications in different problems.

Contents

Methods of neurocomputing and algorithms in conjunction with networks with forward and backward input are examined. Topics include also the supervised and unsupervised learning of neural networks. The examined methods are discussed in the context of application examples such as making various inferences.

Teaching methods

Teaching method Contact Online
Lectures 24 h 0 h
Exercises 10 h 0 h

Teaching language

English

Modes of study

Option 1
Available for:
  • Degree Programme Students
  • Other Students
  • Open University Students
  • Doctoral Students
  • Exchange Students
Lectures, exercises and exam.  Participation in course work 
In English

Written examination and active participation in exercises.

Evaluation

Numeric 1-5.

Study materials

Haykin S., Neural Networks: A Comprehensive Foundation, 2. edition, Prentice Hall, 1999.

Belongs to following study modules

Faculty of Natural Sciences
Faculty of Natural Sciences
Faculty of Natural Sciences
Faculty of Natural Sciences
2017–2018
Teaching
-
Faculty of Natural Sciences