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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.

Introduction to neural network and machine translation

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
8
Кредиты

Преподаватели

Дурандин Олег Владимирович

Дурандин Олег Владимирович

Каляева Екатерина Валерьевна

Каляева Екатерина Валерьевна

Course 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

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

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

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

Assessment Elements

  • non-blocking Control work
  • non-blocking Laboratory work 1
  • non-blocking Laboratory work 2
  • non-blocking Exam
    Итоговый контроль в 2019/2020 учебном году состоялся в 3 модуле.
Interim Assessment

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

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