Теория и практика работы с временными рядами и прогнозированием: от классических статистических наработок до DL и FM

Time Series | Data Fest 2024

Дизайн систем машинного обучения. Введение для начинающих.

ML System Design. Autumn 23/24

ML System Design. Autumn 22/23

Продвинутые подходы и инструменты для разработки и внедрения ML-решений в production.

MLOps и production подход к ML исследованиям 2.0

Голосование за лучших из лучших  в премии сообщества 2022

🎄ODS Awards 2022 ⭐

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.

Natural Language Processing course (stream 3, autumn 2022)

Graph Machine Learning is the science between graph theory and machine learning. It is now a very active area of research, with hundreds of publications each month, impactful applications in the industry, and new insights about our world. Intersection of graph theory and machine learning. Overview, SOTA, applications.

Graph ML

Что сейчас происходит в области обработки естественного языка (NLP)?

NLP in Industry

Цель трека - создание causal inference guide для data scientist-ов. Классические и современные подходы причинно-следственного анализа в применении к задачам машинного обучения.

Causal Inference in ML

Track about Data Science without Machine Learning, Big Data, Neural Networks or anything else requiring training or big amount of storage.

Active