Uniform Manifold Approximation and Projection
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Uniform Manifold Approximation and Projection
Uniform Manifold Approximation and Projection
-
- 0 Rating
- 0 Reviews
- 1 Students Enrolled
Requirements
- MVA book
General Overview
Description
Uniform Manifold Approximation and Projection (UMAP) is a nonlinear dimensionality reduction method based on manifold learning and topological data analysis. It seeks to preserve both local and some global structure when embedding high-dimensional data into a lower-dimensional space. Typical use cases include visualization, clustering, and pattern discovery in datasets from domains such as genomics, image analysis, and natural language processing.
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Meet the instructors !
Matthias Fengler is a full professor of econometrics at the University of St. Gallen, specializing in financial econometrics. He holds a PhD in Quantitative Finance from Humboldt Universität zu Berlin and has experience as both a quantitative analyst and academic. His research focuses on asset pricing, volatility modeling, and risk management. He has published in leading journals such as the Journal of Econometrics, Journal of Financial Econometrics, and Journal of Banking and Finance, and co-authored the sixth edition of Applied Multivariate Statistical Analysis (Springer). His projects include the award-winning “Monitoring Consumption Switzerland,” and his recent work applies machine learning and alternative data, including textual analysis, to financial forecasting.