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Course, academic year 2018/2019
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Neural Networks Implementation I - NAIL060
Title in English: Implementace neuronových sítí I
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2014 to 2019
Semester: winter
E-Credits: 6
Hours per week, examination: winter s.:2/2 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Guarantor: RNDr. Petr Božovský, CSc.
Class: Informatika Mgr. - Teoretická informatika
Classification: Informatics > Theoretical Computer Science
Is co-requisite for: NAIL015
Annotation -
Last update: T_KSI (15.04.2003)
Implementation methods and techniques of neural network models. Backpropagation. Boosting learning efficiency, related and advanced models. Model, topology and network size selection. Adaptive strategies of net optimization. Seminars are devoted to practical issues of specific applications implementation.
Aim of the course -
Last update: RNDr. Petr Božovský, CSc. (07.04.2018)

To learn methods and techniques of implementation of basic models of neuron networks

Course completion requirements -
Last update: RNDr. Petr Božovský, CSc. (16.10.2017)

Student gains a credit after a successful presentation of working programs for the tasks discussed in the course. These programs must be the student's own work, with an eventual utilization of appropriate framework that is under lecturer's approval.

As an integral part of gaining the credit, a sufficient attendance at the seminar is also considered since the task analysis and related discussion take place there.

Literature - Czech
Last update: G_I (28.05.2004)

Beale R.: Neural Computing - An Introduction. Adam Hilger, Bristol, 1990

Goles E.: Lyapunov functions associated to automata networks, in Automata networks in computer science, Princeton University Press, 1987

Tank D., Hopfield J.: Simple "Neural" Optimization Networks, IEEE TCS CAS-33, pp.533-541, 1986

Requirements to the exam -
Last update: RNDr. Petr Božovský, CSc. (16.10.2017)

The examination is in oral form. Student has an opportunity to prepare written notes within the exam to support the oral examination.

Requirements for the examination correspond to the syllabus of the course in the range presented at the lecture.

Syllabus -
Last update: T_KSI (15.04.2003)

Implementation methods and techniques of neural network models. Backpropagation. Boosting learning efficiency, related and advanced models. Model, topology and network size selection. Adaptive strategies of net optimization. Seminars are devoted to practical issues of specific applications implementation.

 
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