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Бакалавриат 2023/2024

Машинное обучение 2

Статус: Курс обязательный (Прикладной анализ данных)
Направление: 01.03.02. Прикладная математика и информатика
Когда читается: 4-й курс, 1, 2 модуль
Формат изучения: без онлайн-курса
Охват аудитории: для своего кампуса
Язык: английский
Кредиты: 5
Контактные часы: 56

Course Syllabus

Abstract

The course "Machine learning 2" is dedicated to the introduction to deep learning and natural language processing problems at the intersection of disciplines such as machine learning, deep learning, and linguistics. The course consists of three parts: (1) introduction to deep learning, (2) the basics part which covers the main concepts, models, and (3) task formulations, and the advanced part that focuses on industrial applications and modern scientific research.
Learning Objectives

Learning Objectives

  • Study what deep learning is, which spheres of AI it embraces and learn the basics of each section
  • Learn to implement neural network models
  • Study basic tasks and methods of natural language processing and text analysis
  • Study modern neural network models for natural language processing
  • Acquire knowledge of software systems and tools for text processing and analysis
Expected Learning Outcomes

Expected Learning Outcomes

  • Be able to apply basic word processing and analysis techniques
  • Be able to formulate and solve problems related to language modeling and specialized problems on text data
  • Know the ethical aspects of word processing
Course Contents

Course Contents

  • Neural Networks. Backpropagation. Gradient Descent. Optimization
  • ‘’Special layers’’. Weights initialization. Regularization.
  • Image Recognition. Convolutional Neural Networks (CNN). Augmentation.
  • Transfer learning. Fine-tuning. TracIn. AutoEncoders.
  • Embeddings. Recurrent Neural Networks (RNN). Transformers.
  • Automatic Speech Recognition.
  • Distillation. Uncertainty Estimation. Quantization. Active Learning.
  • Recommender Systems.
  • NLP Introduction. Statistical text analysis.
  • Vector text representation models
  • Texts classification
  • Sequence labelling
  • Language models
  • Syntax parsing
  • Machine translation
  • Pretrained language models
  • Text generation
  • Text markup, active learning
  • Question-answering systems
  • Multimodal methods
  • Multi-language models
  • Information extraction & information search
  • Text summarization
  • Ethical issues in natural language processing
Assessment Elements

Assessment Elements

  • non-blocking Exam1
  • non-blocking Сolloq1
  • non-blocking HW1
  • non-blocking HW2
  • non-blocking HW3
  • non-blocking HW4
Interim Assessment

Interim Assessment

  • 2023/2024 2nd module
    Final = 0.3*Colloq1 + 0.3*(HW1+HW2+HW3+HW4)+ 0.4*Exam1
Bibliography

Bibliography

Recommended Core Bibliography

  • Deep learning, Goodfellow, I., 2016
  • Introduction to natural language processing, Eisenstein, J., 2019

Recommended Additional Bibliography

  • Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition, Jurafsky, D., 2009

Authors

  • Галевская Софья Андреевна
  • Стоякина Елена Игоревна
  • CHERNYAK EKATERINA LEONIDOVNA