Bachelor 2024/2025

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: distance learning

Online hours: 20

Open to: students of one campus

Instructors: Vitaliy Pozdnyakov, Свиридов Вячеслав Михайлович

Language: English

ECTS credits: 6

Course Syllabus

Abstract

The course introduces basic concepts of neural networks, deep learning and machine translation.

Learning Objectives

• The purpose of the ciyrse is to develop the ability to use neural network in their research and applied projects.

Expected Learning Outcomes

• Is able to use word embedding models
• Is able to use supervised learning
• Understands the advantages and disadvantages of neural networks
• Can create and use convolutional neural networks

Course Contents

• Word embedding, word2vec model
• Supervised learning, logistic regression, multilayer perceptron
• Overfitting problem, regularization
• Convolutional neural networks

Assessment Elements

• Lab 1
• Lab 2
• Lab 3
• Lab 4
• Project
• Quizzes
  Test tasks that are given at every lecture except the first.

Interim Assessment

• 2024/2025 3rd module
  Score = 0.15*Lab 1 + 0.15*Lab 2 + 0.2*Lab 3 + 0.3*Lab 4 + 0.1*Project + 0.1*Quizzes Final score = 0.8*Score + 0.2*Bonus Bonus: A bonus point is awarded if a student has voluntarily gone beyond the scope of the discipline. An example of such work is the application of a method considered as part of an individual project on other data, or the adaptation of a basic model from laboratory 4 with ideas from a recent research on neural networks. The work that can be evaluated for a bonus point is previously a

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

• Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning, 2016. URL: http://www.deeplearningbook.org
• Глубокое обучение. - 978-5-4461-1537-2 - Николенко С., Кадурин А., Архангельская Е. - 2020 - Санкт-Петербург: Питер - https://ibooks.ru/bookshelf/377026 - 377026 - iBOOKS

Authors

• Klimova Margarita Andreevna
• MALAFEEV Aleksei Iurevich