2026/2027
Современные методы анализа данных
ID 1121978
Статус:
Маго-лего
Где читается:
Факультет компьютерных наук
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Игнатов Дмитрий Игоревич
Язык:
русский
Кредиты:
6
Контактные часы:
56
Программа дисциплины
Аннотация
The course “Modern Methods of Data Analysis” introduces students to a new and actively evolving interdisciplinary field of modern data analysis. Started as a branch of Artificial Intelligence, it attracted attention not only from mathematicians and computer scientists, but also that of physicists, economists, computational biologists, linguists and others and became a truly interdisciplinary field of study. In spite of the variety of data sources that could be analyzed, objects and attributes that form a particular dataset possess common statistical and structural properties. The interplay between known data and unknown ones gives rise to complex pattern structures and machine learning methods that are the focus of the study. In the course we will consider methods of Machine Learning and Data Mining. Special attention will be given to the hands-on practical analysis of the real world datasets using available software tools and modern programming languages and libraries.
Цель освоения дисциплины
- Learning objectives of the course “Modern Methods of Data Analysis” (MMDA) are to familiarize students with a new rapidly evolving field of machine learning and mining, and provide practical knowledge experience in analysis of real world data.
Планируемые результаты обучения
- Know basic notions and terminology used in Machine Learning and Data Mining
- Understand fundamental principles of modern data analysis
- Learn to develop mathematical models of Machine Learning and Data Mining
- Be capable of analyzing real world data
Содержание учебной дисциплины
- Introduction to Machine Learning and Data Mining
- Clustering and its basic techniques
- Classification and its basic techniques
- Frequent Itemset Mining and Association Rules
- Regression and its basic techniques
- Feature Selection and Dimensionality Reduction. Outlier detection.
- Recommender Systems and Algorithms
- Ensemble Clustering and Classification
- Multimodal relational clustering
Элементы контроля
- Homework 1. Classification.
- Homework 2. Clustering.
- Homework 3. Frequent Closed Itemsets and Association Rules.
- Homework 4. Recommender Systems.
- Data Science Project
Промежуточная аттестация
- 2026/2027 4th module0,8*(0,5 * homework + 0,5 * project report) + 0,2 * exam
Список литературы
Рекомендуемая основная литература
- The elements of statistical learning : data mining, inference, and prediction, Hastie, T., 2017
Рекомендуемая дополнительная литература
- Han, J., Kamber, M., Pei, J. Data Mining: Concepts and Techniques, Third Edition. – Morgan Kaufmann Publishers, 2011. – 740 pp.