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Regular version of the site

Introduction to neural network and machine translation

2021/2022
Academic Year
ENG
Instruction in English
8
ECTS credits
Delivered at:
Department of Applied Mathematics and Informatics (Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod))
Course type:
Elective course
When:
4 year, 2, 3 module

Instructors


Durandin, Oleg


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 3rd module
    0.15 * Laboratory work 2 + 0.4 * Exam + 0.3 * Control work + 0.15 * Laboratory work 1
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