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Natural Language Processing course (stream 1, autumn 2021)

Natural Language Processing (NLP) is a domain of research whose objective is to analyze and understand human languages and develop technologies to enable human machine interactions with natural languages. NLP is an interdisciplinary field involving linguistics, computer sciences and artificial intelligence. The goal of this course is to provide students with comprehensive knowledge of NLP. Students will be equiped with the principles and theories of NLP, as well as various NLP technologies, including rule-based, statistical and neural network ones. After this course, students will be able to conduct NLP research and develop state-of-the-art NLP systems.

Official support channel: #course_nlp_huawei in ODS.ai slack. If you are not registered, please join the community.

Lecturers:

Valentin Malykh (personal page)

Logistics:

The lectures are presented at 10:15 MSK each Tuesday online and seminars at 18:30 MSK each Tuesday online.
Course started: 14th of September 2021.
The main lectures end: 16th of November 2021.

Prerequisites

  • Basic Python Programming
  • College Calculus, Linear Algebra
  • Basic Probability and Statistics
  • Foundations of Machine Learning

Assignments

There will be two assignments on the course.

Projects

The participants will be suggested to work on a project during the course. The successful project development is crucial to pass the course.

The projects from previous semester are available here.

Валентин Малых

Track program

Final Projects

Final Projects

November 30

December 14

2
Unit 1: Introduction and Basic Text Processing. Lecture 01

Unit 1: Introduction and Basic Text Processing. Lecture 01

Introduction to Natural Language Processing

31
Unit 2: NLP Techniques. Lecture 02

Unit 2: NLP Techniques. Lecture 02

Machine Learning Basics and Text Classification

Unit 2: NLP Techniques. Lecture 03

Unit 2: NLP Techniques. Lecture 03

Word Embeddings

Unit 2: NLP Techniques. Lecture 04

Unit 2: NLP Techniques. Lecture 04

Convolutional Neural Networks

Unit 2: NLP Techniques. Lecture 05

Unit 2: NLP Techniques. Lecture 05

Hidden Markov Models and Tagging

Unit 2: NLP Techniques. Lecture 06

Unit 2: NLP Techniques. Lecture 06

Recurrent Neural Networks

Unit 2: NLP Techniques. Lecture 07

Unit 2: NLP Techniques. Lecture 07

Context-Free Grammars and Parsing

Unit 3: NLP Applications. Lecture 08

Unit 3: NLP Applications. Lecture 08

Statistical Machine Translation

Unit 3: NLP Applications. Lecture 09

Unit 3: NLP Applications. Lecture 09

Transformers

Practice 2

Practice 2

The task of to classifying toxic comments

November 22

December 7

Unit 3: NLP Applications. Lecture 10

Unit 3: NLP Applications. Lecture 10

Conversational AI

Unit 3: NLP Applications. Lecture 11

Unit 3: NLP Applications. Lecture 11

Knowledge Graphs

Unit 3: NLP Applications. Lecture 12

Unit 3: NLP Applications. Lecture 12

Information Retrieval

4

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