Introduction to neural network and machine translation

2025/2026

Учебный год

ENG

Обучение ведется на английском языке

6

Кредиты

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

Преподаватель

Поздняков Виталий Витальевич

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
• Can create and use recurrent neural networks
• Can create and use attention-based neural networks
• Can pretrain and fine-tune neural networks and their components
• Understands the principles of large language models and knows how to use them to solve applied problems

Course Contents

• Word embedding, word2vec model
• Supervised learning, logistic regression, multilayer perceptron
• Overfitting problem, regularization
• Convolutional neural networks
• Recurrent neural networks, Seq2seq modeling
• Attention-based models, Transformers
• Pretraining and fine-tuning, BERT, GPT
• Large language models, Prompt engineering, Chain-of-thought

Assessment Elements

• Quizzes
• Laboratory work
• Individual project
• Activity bonus
• Labs bonus

Interim Assessment

• 2025/2026 3rd module
  0.05 * Activity bonus + 0.05 * Activity bonus + 0.4 * Individual project + 0.15 * Laboratory work + 0.15 * Laboratory work + 0.05 * Labs bonus + 0.05 * Labs bonus + 0.05 * Quizzes + 0.05 * Quizzes

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
• Ian Goodfellow, Yoshua Bengio, & Aaron Courville. (2016). Deep Learning. The MIT Press.
• 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
• Pozdniakov Vitalii Vitalevich