CPSC 303: Numerical Approximation and Discretization (2015W T2)

LFD is a data-driven discretization technique that does not require any user input. LFD uses low frequency values as cut points and thus reduces the information loss due to discretization. It uses all other categorical attributes and any numerical attribute that has already been categorized.

This project is an initiative to define several basic neutral file formats to facilitate exchange of meshes (as in FEM) between different SW packages.

Discretization of Normal random variables and simulations

Quick Layered Correlation-based Feature Filtering

Built a voice-controlled car from scratch incorporating machine learning methods such as the Euclidean Classifier and k-NN Classifier, open and closed loop feedback control systems, principal component analysis, regression analysis, and transient analysis

Evenly spaced piecewise linear interpolation of functions represented using symbols in Python

Various Problems in Digital Control Systems

Kernel density integral transformation: feature preprocessing and univariate clustering

Notes on discretization and numerical solutions to differential equations

Classification by Voting Feature Intervals in Python

JMat is a library which implements fundamental matrix capabilities in Java.

Notebooks containing the PDGeoFF project's implementations in FEniCS. “The project leading to this application has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 896616.”

Times Series, Classification-label/image, Regression

Discrete Differential Forms in arbitrary dimensions

Discretization of numeric literals in RDF via SPARQL

The project encompasses the building of a data classification and clustering system, followed by EDA - Exploratory Data Analysis, and is concluded with the presentation of the results.