Introduction to neural network and machine translation

2024/2025

Academic Year

ENG

Instruction in English

6

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

Pozdnyakov, Vitaliy

Свиридов Вячеслав Михайлович

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

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