Statistical Machine Intelligence & Learning Engine

Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)

Implemented Machine Learning Algorithms in Hyperbolic Geometry (MDS, K-Means, Support vector machines, etc.)

Comparison-based Machine Learning in Python

This module allows users to analyze k-means & hierarchical clustering, and visualize results of Principal Component, Correspondence Analysis, Discriminant analysis, Decision tree, Multidimensional scaling, Multiple Factor Analysis, Machine learning, and Prophet analysis.

Unsupervised Learning

Analysis of Data Scientist Job Descriptions using Natural Language Processing

This routine is implemented in Matlab

Module for Niek Veldhius, Sumerian Text Analysis.

Analyzing the historical cryptocurrency trading dataset, to portrait its dynamic landscape and dig into features of crypt currencies to figure out if any patterns in their price movement.

Showcasing Manifold Learning with ISOMAP, and compare the model to other transformations, such as PCA and MDS.

App for fast NMDS plots

This repository contains materials associated to the course "Multivariate Analysis" taught at the Faculty of Mathematics and Statistics (FME), UPC under the MESIO-UPC-UB Interuniversity Program under the instructors "Ferran Revertar", "Miguel Salicru" and "Jan Graffelman"

Experiments in NLOS mitigitation under MDS-based RF Positioning

The code for Multidimensional Scaling (MDS), Sammon Mapping, and Isomap.

Using R & VoteView mutlidimensional scaling (MDS) methods for the analysis & visualization of complex patterns of crosslinguistic variation.

A library for the Analysis of Molecular Dynamics Simulations of Self Assembling Peptides. Started during an internship at CNTE, Niguarda Hospital, Milan

Extendible metric MDS in Python

Collective Project is one of our required courses for master degree in Mathematics which comprises of 5 members each. In this our group, we are working on Multidimensional Scaling: Multidimensional Scaling is a set of procedures that allows the researcher to map distances between objects in a multidimensional space into a lower-dimensional space…

Topological optimization for continuous phenotype mapping. When you have cross-reactivity or binding affinity measurements between some pairs of items in the data, with even large proportions of missing data, use Topolow to find the full coordinates of all items, in a space with optimal dimensionality.