Bachelor 2021/2022

Introduction to neural network and machine translation

Category 'Best Course for Career Development'

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'

Category 'Best Course for New Knowledge and Skills'

Type: Elective course (Fundamental and Applied Linguistics)

Area of studies: Fundamental and Applied Linguistics

Delivered by: Department of Applied Mathematics and Informatics

Where: Faculty of Humanities (Nizhny Novgorod)

When: 4 year, 2, 3 module

Mode of studies: offline

Open to: students of one campus

Instructors: Oleg Durandin, Каляева Екатерина Валерьевна

Language: English

ECTS credits: 8

Contact hours: 38

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

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

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, Д. Библиотека Keras – инструмент глубокого обучения. Реализация нейронных сетей с помощью библиотек Theano и TensorFlow / Д. Антонио, П. Суджит ; перевод с английского А. А. Слинкин. — Москва : ДМК Пресс, 2018. — 294 с. — ISBN 978-5-97060-573-8. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/111438 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.