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Обычная версия сайта
2025/2026

Научно-исследовательский семинар "Топологическая обработка данных"

Статус: Дисциплина общефакультетского пула
Когда читается: 3, 4 модуль
Охват аудитории: для всех кампусов НИУ ВШЭ
Язык: английский
Кредиты: 6
Контактные часы: 72

Course Syllabus

Abstract

Topological Data Analysis (TDA) is a field that lies at the intersection of data analysis, algebraictopology, computational geometry, computer science, statistics, and other related areas. The main goal of TDAis to use ideas and results from geometry and topology to develop tools for studying qualitative features ofdata. To achieve this goal, one needs precise definitions of qualitative features, tools to compute them inpractice, and some guaranteeabout the robustness of those features. One way to address all three points is amethod in TDA called persistent homology (PH). This method is appealing for applications because it is basedon algebraic topology, which gives a well-understood theoretical framework to study qualitative features ofdata with complex structure, is computable via linear algebra, and is robust with respect to small perturbationsin input data
Learning Objectives

Learning Objectives

  • The main goal of TDA is to use ideas and results from geometry and topology to develop tools for studying qualitative featuresof data.
Expected Learning Outcomes

Expected Learning Outcomes

  • ---
Course Contents

Course Contents

  • Simplicial complexes.
  • Homologies of simplicial complexes.
  • Theory of persistent modules.
  • Persistent homologies of a filtered simplicial complex.
  • Persistent Laplacian.
  • Stability of persistent homology and persistent Laplacian.
Assessment Elements

Assessment Elements

  • non-blocking presentation
  • non-blocking computational project
Interim Assessment

Interim Assessment

  • 2025/2026 4th module
    The course will cover the core material, offering presentations based on journal articlesand computational projects. Grading will be based on the presentation, which must demonstrate mastery ofthe material, and the computational project, which serves as an illustration of the presentation’s material.Students can take an exam covering the core material instead of the presentation, demonstrating their use ofstandard libraries for calculating stable homology. In this case, the grade will not exceed 7.
Bibliography

Bibliography

Recommended Core Bibliography

  • Bayesian data analysis, Gelman, A., 2014

Recommended Additional Bibliography

  • Multivariate data analysis, , 2019

Authors

  • GORBUNOV VASILIY GENNADEVICH