Bachelor
2021/2022
Machine Learning 2
Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type:
Compulsory course
Area of studies:
Applied Mathematics and Information Science
Delivered by:
Big Data and Information Retrieval School
Where:
Faculty of Computer Science
When:
4 year, 1-3 module
Mode of studies:
distance learning
Open to:
students of one campus
Instructors:
Ekaterina Artemova,
Ilia Karpov,
Veronika Sarkisyan,
Akim Tsvigun,
Никишина Ирина Александровна
Language:
English
ECTS credits:
7
Contact hours:
90
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.
