2025/2026
Обработка естественного языка II
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Статус:
Маго-лего
Где читается:
Санкт-Петербургская школа экономики и менеджмента
Когда читается:
2 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Сурков Антон Юрьевич
Язык:
английский
Кредиты:
3
Контактные часы:
28
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.
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
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