Магистратура
2024/2025
Современные методы анализа данных
Статус:
Курс по выбору (Науки о данных (Data Science))
Направление:
01.04.02. Прикладная математика и информатика
Где читается:
Факультет компьютерных наук
Когда читается:
1-й курс, 3, 4 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Игнатов Дмитрий Игоревич
Прогр. обучения:
Науки о данных
Язык:
английский
Кредиты:
6
Course Syllabus
Abstract
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.
Learning Objectives
- 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.
Expected Learning Outcomes
- 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
Course Contents
- 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
Assessment Elements
- Homework 1. Classification.
- Homework 2. Clustering.
- Homework 3. Frequent Closed Itemsets and Association Rules.
- Homework 4. Recommender Systems.
- Data Science Project
