FI:P021 Neural Networks - Course Information

P021 Neural Networks

Faculty of Informatics
Autumn 1998

Extent and Intensity

2/2. 4 credit(s). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).

Teacher(s)

Jiří Šíma (lecturer)

Prerequisites

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

Syllabus

Introduction to Neural Networks. History of neurocomputing; neurophysiological motivations; mathematical model of neural network: formal neuron, organizational, active, and adaptive dynamics; position of neural networks in computer science: comparison with von Neumann computer architecture, applications, implementations, neurocomputers.
Classical Models of Neural Networks. Perceptron: convergence; multi-layered network and backpropagation strategy: choice of topology and generalization; MADALINE: Widrow learning rule.
Associative Neural Networks. Linear associative network: Hebb law and pseudohebbian adaptation; Hopfield network: energy, capacity; continuous Hopfield network: traveling salesman problem; Boltzmann machine: simulated annealing, equilibrium.
Self-Organization. Kohonen network: unsupervised learning; Kohonen maps; counterpropagation: Grossberg learning rule.
Seminar: Software implementation of particular neural network models and their simple applications.

Language of instruction

Czech

Further Comments

The course is taught annually.
The course is taught: every week.

The course is also listed under the following terms Autumn 1995, Autumn 1996, Autumn 1999, Spring 2001, Autumn 2001.