Кафедра прикладной математики и информатики (Нижний Новгород)

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Публикации

Книга

From Approximate Variation to Pointwise Selection Principles

Vyacheslav V. Chistyakov.

Springer, 2021.

Статья

Planning of life-depleting preventive maintenance activities with replacements

Ketkov S., Prokopyev O. A., Maillart L. M.

Annals of Operations Research. 2022.

Глава в книге

Faster exploration of some temporal graphs

Adamson D., Gusev V. V., Malyshev D. et al.

In bk.: 1st Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2022, March 28–30, 2022, Virtual Conference). Vol. 221. Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, 2022. Ch. 5. P. 5:1-5:10.

Препринт

The approximate variation of univariate uniform space valued functions and pointwise selection principles

Vyacheslav V. Chistyakov, Svetlana A. Chistyakova.

Functional Analysis. arXiv [math.FA]. Cornell University, NY, USA, 2020. No. 2010.11410.

Все публикации

Кто читает:: Кафедра прикладной математики и информатики (Нижний Новгород) (Факультет информатики, математики и компьютерных наук (Нижний Новгород))

Статус:: Курс по выбору

Когда читается:: 4-й курс, 2, 3 модуль

Преподаватели

Дурандин Олег Владимирович

Каляева Екатерина Валерьевна

Full Syllabus

Abstract

The course introduces students to the basic concepts of data analysis and machine learning and the application of data mining and machine learning to solve practical problems in the professional field

Learning Objectives

The purpose of learning is development of skills to conduct research, including problem analysis, setting goals and objectives, understanding of the object and subject of study, choice of method and research methods, as well as evaluating its quality.

Expected Learning Outcomes

Able to work with python ibraries
Has an idea about the regularization features
Has an idea of principles of building neural networks
Has an idea of the basic concepts
Has an idea of the features of convolutional networks
Has an idea of the principles of neural networks training

Course Contents

The simplest methods of machine learning
The loss function. Regularization. Optimization.
Method of back propagation. Neural networks.
Training of neural networks. Normalization methods.
Convolutional neural network architectures.
Libraries for training and running neural networks.

Assessment Elements

Control work
Laboratory work 1
Laboratory work 2
Exam
Итоговый контроль в 2019/2020 учебном году состоялся в 3 модуле.

Interim Assessment

2021/2022 2nd module
2021/2022 3rd module
0.3 * Control work + 0.4 * Exam + 0.15 * Laboratory work 1 + 0.15 * Laboratory work 2

Bibliography

Recommended Core Bibliography

Kelleher, J. D. (2019). Deep Learning. Cambridge: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2234376

Recommended Additional Bibliography

Антонио Джулли, Суджит Пал - Библиотека Keras – инструмент глубокого обучения. Реализация нейронных сетей с помощью библиотек Theano и TensorFlow - Издательство "ДМК Пресс" - 2018 - ISBN: 978-5-97060-573-8 - Текст электронный // ЭБС ЛАНЬ - URL: https://e.lanbook.com/book/111438

Introduction to neural network and machine translation