Infopaket

Neural networks and neurocomputation

ДВС.3.08

Вибіркова дисципліна для ОП

First

2023/2024

8 Semester

4

PRN16. Perform parallel and distributed calculations, apply numerical methods and algorithms for parallel structures, parallel programming languages ​​in the development and operation of parallel and distributed software. PRN20.3. Know the means of implementing multi-process calculations.

Know: discrete mathematics, probability theory, mathematical statistics, data analysis, intelligent data processing and the basics of programming in the scope of standard university courses. Be able to: apply knowledge from the above disciplines to solving problems. Possess elementary skills: working with a computer

The discipline is a selective component of the OP training of specialists at the first (bachelor) level of higher education in the field of knowledge 12 "Information technologies" from the specialty 122 "Computer science", educational and professional program "Informatics", selective block "Information technologies and systems". It is taught in the 8th semester, the volume is 120 hours. (4 ECTS credits), of which lectures – 40 hours, consultations – 2 hours, independent work – 78 hours. As a result of studying the academic discipline, the student must: to know the basic concepts of biological and artificial neural networks, methods and algorithms of learning neural networks, principles of their implementation in programming languages, application in applied tasks. to be able to apply in practice the methods and algorithms of learning artificial neural networks, to solve educational and practical problems, to justify one's own view on the solution of the problem, to communicate with colleagues on programming issues, to make reports on solving problems. The discipline uses concepts from discrete mathematics, probability theory and mathematical statistics, principles of programming, intelligent data processing.

Osnovnі 1. Anisimov A.V. Informatika. Tvorchestvo. Rekursiia. – K.: Naukova dumka, 1988. – 224 s. 2. Amosov N.M. Algoritmy razuma. — K.: Naukova dumka, 1979. — 223 s. 3. E.V. Bodianskii, O.G.Rudenko. Iskusstvennye neironnye seti: arkhitektury, obuchenie, primeneniia. – Khar-kov, 2004. 4. Subbotіn S. O. Neіterativnі, evoliutsіinі ta mul-tiagentnі metodi sintezu nechіtkologіchnikh і neiromerezhnikh modelei : monografіia / S. O. Subbotіn, A. O. Olіinik, O. O. Olіinik ; pіd zag. red. S. O. Subbotіna. – Zaporіzhzhia : ZNTU, 2009. – 375 s. 5. Khaikin S. Neironnye seti: polnyi kurs, 2-e izdanie. – M.: Izd-vo “Vil-iams”. – 2006. – 1104 c. 6. Rutkovskaia D., Pilins-kii M., Rutkovskii L. Neitronnye seti, geneticheskie algoritmy i nechetkie sistemy. M.: Izd-vo “Goriachaia liniia-Telekom”. – 2006. – 384 c. 7. Gorban- A.N. Obuchenie neironnykh setei. M.: SP ParaGraf. – 1990. – 152 c. ..

Lectures, consultations, independent work

- semester assessment: Eighth semester 1. Control work 1: РН1.1, РН1.2, РН2.1, РН2.2 – 20 points/12 points. 2. Control work 2: РН1.1, РН1.2, РН2.1, РН2.2 – 20 points/12 points. 3. Current evaluation: РН1.1, РН1.2, РН2.1, РН2.2, РН3.1, РН4.1, РН4.2 – 20 points/12 points. - final evaluation (in the form of an exam): - the maximum number of points that can be obtained by a student: 40; - learning outcomes that are evaluated: PH1.1, PH1.2, PH2.1, PH3.1; - form of conduct: written work; - types of tasks: task (40%), theoretical question (60%). A student is admitted to the exam if he scored at least 36 points in the semester. For obtaining an overall positive grade in the discipline, the grade for the exam must be at least 24 points.

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