Machine Learning 2

2021/2022

Учебный год

ENG

Обучение ведется на английском языке

7

Кредиты

Лучший по критерию «Полезность курса для Вашей будущей карьеры»

Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»

Кто читает:

Департамент больших данных и информационного поиска

Статус:

Курс обязательный

Когда читается:

4-й курс, 1-3 модуль

Преподаватели

Артемова Екатерина Леонидовна

Карпов Илья Андреевич

Никишина Ирина Александровна

Саркисян Вероника Вагановна

Цвигун Аким Олегович

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

• 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

• 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

• 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.
• Ethical issues in natural language processing.

Assessment Elements

• Quizzes
• Homeworks
• Exam

Interim Assessment

• 2021/2022 3rd module
  0.3 * Homeworks + 0.5 * Exam + 0.2 * Quizzes

Authors

• CHERNYAK EKATERINA LEONIDOVNA