Дизайн систем машинного обучения. Введение для начинающих.

ML System Design. Autumn 22/23

Цель трека - создание сообщества для решения актуальных проблем интерпретируемости моделей машинного обучения и выявления причинно-следственных связей в них.

Interpretable & Causal ML

Инструменты и практики управления экспериментами в Machine Learning, обеспечения воспроизводимости (Reproducibility) и автоматизации процессов.

Machine Learning REPA: Reproducibility, Experiments and Pipelines Automation

Трек для глубокого изучения рекомендательных систем на практике

Your Second RecSys

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 1, autumn 2021)

Курс по графам знаний (Knowledge Graphs) и как их готовить в 2021 году.

Active

ML System Design. Autumn 22/23

Interpretable & Causal ML

Machine Learning REPA: Reproducibility, Experiments and Pipelines Automation

Your Second RecSys

Natural Language Processing course (stream 1, autumn 2021)

KG Course 2021