Description:

This seminar provides a comprehensive overview of dimension reduction techniques in R and Python for high-dimensional complex datasets, focusing on their practical applications. Participants will gain theoretical knowledge and practical experience in linear and nonlinear methods such as PCA, tSNE and UMAP. By the conclusion of the seminar, participants will understand the theoretical and practical foundations of these methods, with a wealth of examples that can be rapidly applied for their own research problems.

Start: Wednesday, 26 February 2025 @ 19:00

End: Thursday, 27 February 2025 @ 01:00

Timezone: Sydney

Learning Objectives:

Topics Covered
High-dimensional biological data , Curse of Dimensionality , data-driven choice of statistical analysis , Overview of dimension reduction techniques , Principal Component Analysis (PCA) , Multi-Dimensional Scaling (MDS) , matrix factorization , JackStraw , estimating number of informative PCs , coding PCA from scratch , Tracy-Widom approach , tSNE , UMAP , Autoencoders , PHATE , neighborhood graph , tuning hyperparameters , multiOmics , Omics integration , heterogeneous biological data , synergistic effects , graph intersection method , applications in single cell genomics

Eligibility:
  • Open to all

Organiser: Nikolay Oskolkov, Molecular Biosciences, Lund University

Contact: info@instats.org

Host institution: Instats

Linear and Nonlinear Dimensionality Reduction with Nikolay Oskolkov, Molecular Biosciences, Lund University https://dresa.org.au/events/linear-and-nonlinear-dimensionality-reduction-with-nikolay-oskolkov-molecular-biosciences-lund-university This seminar provides a comprehensive overview of dimension reduction techniques in R and Python for high-dimensional complex datasets, focusing on their practical applications. Participants will gain theoretical knowledge and practical experience in linear and nonlinear methods such as PCA, tSNE and UMAP. By the conclusion of the seminar, participants will understand the theoretical and practical foundations of these methods, with a wealth of examples that can be rapidly applied for their own research problems. 2025-02-26 19:00:00 UTC 2025-02-27 01:00:00 UTC Nikolay Oskolkov, Molecular Biosciences, Lund University Instats info@instats.org [] [] [] open_to_all []