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Integral Robot Technologies and Speech Behavior

Kharlamov A. A., Pantiukhin D., Borisov V. et al.

Newcastle upon Tyne: Cambridge Scholars Publishing, 2024.

On Trees with a Given Diameter and the Extremal Number of Distance-k Independent Sets

D. S. Taletskii.

Journal of Applied and Industrial Mathematics. 2023. Vol. 17. No. 3. P. 664-677.

Глава в книге
Uncertainty of Graph Clustering in Correlation Block Model

Artem Aroslankin, Valeriy Kalyagin.

In bk.: Mathematical Optimization Theory and Operations Research: Recent Trends. 22nd International Conference, MOTOR 2023, Ekaterinburg, Russia, July 2–8, 2023, Revised Selected Papers, vol 1881. Springer, 2023. P. 353-356.

Independent sets versus 4-dominating sets in outerplanar graphs

Taletskii D.

math. arXiv. Cornell University, 2023

Introduction to neural network and machine translation

Учебный год
Обучение ведется на английском языке


Course Syllabus


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

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

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

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

Assessment Elements

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

Interim Assessment

  • 2023/2024 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 agreed with the teacher.A student gets a maximum score of 10 if he/she has successfully completed at least two works that deserve a bonus.


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.
  • Николенко С., Кадурин А., Архангельская Е. - Глубокое обучение. — (Серия «Библиотека программиста») - 978-5-4461-1537-2 - Санкт-Петербург: Питер - 2020 - 377026 - https://ibooks.ru/bookshelf/377026/reading - iBOOKS