We are excited to welcome Prof. Alejandro Rodriguez Garcia from the Abdus Salam International Centre for Theoretical Physics (ICTP) to Enabla! In his lecture, Alex continues the topic started by Marcello and explores the use of unsupervised machine learning techniques in many-body quantum systems, highlighting how dimensionality reduction can illuminate structure within complex data. Particular emphasis is placed on the Principal Component Analysis (PCA) as a key method to maximize variance while reducing dimensionality. This lecture sets the stage for future topics such as clustering and manifold learning.

🎥 Join us for this #OpenAccess lecture and take advantage of Enabla's unique features to ask questions directly to Prof. Rodriguez Garcia and engage in discussions with the community: https://enabla.com/pub/1112/about

Don't miss this opportunity to enhance your knowledge in the intersection of data mining and quantum physics!

#MachineLearning #UnsupervisedLearning #DimensionalityReduction #QuantumSystems #PCA #DataMining #OpenScience

Biologists, stop putting UMAP plots in your papers

#UMAP is a powerful tool for exploratory data analysis, but without a clear understanding of how it works, it can easily lead to confusion and misinterpretation.

https://simplystatistics.org/posts/2024-12-23-biologists-stop-including-umap-plots-in-your-papers/

#clustering #dimensionalityreduction #dataviz

We just completed a new course on #DimensionalityReduction in #Neuroscience, and the full teaching material 🐍💻 is now freely available (CC BY 4.0 license):

🌍 https://www.fabriziomusacchio.com/blog/2024-10-24-dimensionality_reduction_in_neuroscience/

The course is designed to provide an introductory overview of the application of dimensionality reduction techniques for neuroscientists and data scientists alike, focusing on how to handle the increasingly high-dimensional datasets generated by modern neuroscience research.

#PythonTutorial #CompNeuro

These four #Python #tutorials introduce and discuss #PCA, #tsne, #factoranalysis, and #Autoencoder as powerful tools for #DimensionalityReduction:

🌍 https://www.fabriziomusacchio.com/blog/2023-06-16-pca_with_python/
🌍 https://www.fabriziomusacchio.com/blog/2023-06-12-tsne_vs_pca/
🌍 https://www.fabriziomusacchio.com/blog/2023-06-16-factoranalysis_with_python/
🌍 https://www.fabriziomusacchio.com/blog/2023-06-16-autoencoder_with_python/

Feel free to share, use and remix 😊🙏

#tutorial

“Regardless of how we do dimensionality reduction, if the assumptions and biases underlying a method are not understood then it can be possible to see things in the data that aren’t there. “

#PCA #DimensionalityReduction #statistics

https://doi.org/10.1073/pnas.2319169120

Why the simplest explanation isn’t always the best: #DimensionalityReduction such as #PCA can see structures that do not exist and miss structures that exist. The simplest explanation isn’t always the best.

✍️ Dyer & Kording ( @kordinglab) (2023)
🌍 https://www.pnas.org/doi/10.1073/pnas.2319169120

#DataAnalysis #CompNeuro