Natural Language Processing II

2025/2026

Academic Year

ENG

Instruction in English

3

ECTS credits

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'

Delivered at:

Department of Business Informatics and Operations Management

Course type:

Elective course

When:

1 year, 2 module

Instructor

Surkov, Anton

Course Syllabus

Abstract

Prerequisites: strong knowledge and skills in Python (numpy, pandas, scikit-learn), mathematical statistics, and machine learning modeling. Natural language processing (NLP) is an important field of computer science, artificial intelligence and linguistics aimed at developing systems that are able to understand and generate natural language at the human level. Modern NLP systems are predominantly based on machine learning (ML) and deep learning (DL) algorithms, and have demonstrated impressive results in a wide range of NLP tasks such as summarization, machine translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic modeling. We interact with such systems and use products involving NLP on a daily basis which makes it exciting to learn how these systems work. This course covers the main topics in NLP, ranging from text preprocessing techniques to state-of-the-art neural architectures. We hope to facilitate interest in the field by combining the theoretical basis with the practical applications of the material.

Learning Objectives

• Student knows how modern LLMs, RAG systems and agentic systems work

Expected Learning Outcomes

• Student is able to work with modern encoder transformers to build retrieval systems and create text representations.
• To understand all stages of modern LLMS' training process and know about efficient inference techniques.
• To understand how modern RAG systems work.
• To understand how to build Agents based on LLMs

Course Contents

• Transformers: encoders
• Modern LLMs: training and inference.
• Modern LLMs: RAG
• Modern LLMs: Agents

Assessment Elements

• Homework
• Test

Interim Assessment

• 2025/2026 2nd module
  0.4 * Homework + 0.6 * Test

Bibliography

Recommended Core Bibliography

• 9780262046824 - Kevin P. Murphy - Probabilistic Machine Learning - 2022 - MIT Press - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=2932689 - nlebk - 2932689
• Machine learning : beginner's guide to machine learning, data mining, big data, artificial intelligence and neural networks, Trinity, L., 2019
• Transformers for machine learning : a deep dive, Kamath, U., 2022

Recommended Additional Bibliography

• Machine learning fundamentals : a concise introduction, Jiang, H., 2021

Authors

• Орлова Екатерина Дмитриевна
• SURKOV ANTON YUREVICH