Neural Networks 1

Garant předmětu:

Kateřina Horaisová

Lecturer:

Kateřina Horaisová

Tutor:

Kateřina Horaisová

Supervisor:

Department of Software Engineering

Synopsis:

Mathematical analysis, model theory and biological context are used for construction of simple models of neural structures. The models are able to learn from pattern sets and their structures and parameters are subjects of optimization.

Requirements:

Basic knowledges from linear algebra.

Syllabus of lectures:

1.Biological neural networks and their models.

2.Artificial neural networks, basic terms.

3.ANN topology, acyclic and hierarchic networks

4.Bipolar perceptron as switching element.

5.Logical function as perceptron network.

6.Hebb learning, LSQ learning, pseudoinversion, OLAM.

7.Robust learning principles, pruning.

8.Rosenblatt learning, Widrow delta learning.

9.Non-linear preprocessing and Cover theorem.

10.Smooth perceptron, delta rule, stochastic gradient method.

11. Support Vector Machine

Syllabus of tutorials:

1.Biological neural networks and their models.

2.Artificial neural networks, basic terms.

3.ANN topology, acyclic and hierarchic networks

4.Bipolar perceptron as switching element.

5.Logical function as perceptron network.

6.Hebb learning, LSQ learning, pseudoinversion, OLAM.

7.Robust learning principles, pruning.

8.Rosenblatt learning, Widrow delta learning.

9.Non-linear preprocessing and Cover theorem.

10.Smooth perceptron, delta rule, stochastic gradient method.

11. Support Vector Machine

Study Objective:

Knowledge:

Elements of artificial neural networks.

Abilities:

Representation of logical function as perceptron network, use of algorithms for weights calculation of perceptron network, use of support vector machine.

Study materials:

Compulsory literature:

[1] J. Šíma, R. Neruda: Teoretické otázky neuronových sítí, Matfyzpress, Praha, 1996.

[2] M. Šnorek: Neuronové sítě a neuropočítače, ČVUT, Praha 2002

Recommended literature:

[3] S. Haykin: Neural Networks, Macmillan, New York, 1994.

[4] L.V. Fausett: Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall, New Jersey, 1994.

Note:

Time-table for winter semester 2023/2024:

Time-table is not available yet

Time-table for summer semester 2023/2024:

Time-table is not available yet

The course is a part of the following study plans:

• Applications of Informatics in Natural Sciences (elective course)