High-Dimensional Statistics D
Aalto University
<p>Modern applications of data science and machine learning commonly deal with high-dimensional data in which the number variables is large. This course focuses on mathematical concepts developed for understanding the interplay of dimensions, sparsity, and complexity in such datasets and associated stochastic models. Key concepts:</p><ul><li>Sub-Gaussian and sub-exponential probability distributions.</li><li>Concentration inequalities of random matrices and their singular values.</li><li>Random projections in Euclidean spaces.</li><li>Covering numbers and metric entropy of sets.</li><li>Applications to dimension reduction, sparse regression, and network clustering.</li></ul>
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Course dates
24 February 2026 - 13 April 2026
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Course organizer
Lasse Leskelä
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Place/Venue
School of Science / Department of Mathematics and Systems Analysis
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City
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Country
Finland
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Workload
5
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Link
https://mycourses.aalto.fi/course/search.php?...
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