Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

Three C++ projects assigned for the Numerical Methods for Electrical Engineering (EE 242) course in the Spring 2021 semester.

The code for generalized eigenvalue problem

Imperial College London »Mathematics for Machine Learning«. A sequence of 3 courses on the prerequisite mathematics for applications in data science and machine learning. (1) Linear Algebra (2) Multivariate Calculus and (3) Principal Component Analysis (completed Sept. 10th, 2018)

A library providing modules with easy to use matrix operations for real(8) and complex(8) arrays

ForEig - A Fortran library for eigenvalue and eigenvector calculations.

The software is an implementation of the enriched subspace iteration method for solving the generalized eigenvalue problems.

Assorted C++-ified LAPACK routines for reference.

Project written in C++ on basic simulations of eigenstates of the Hamiltonian in the One Dimensional Schrödinger Equation for common potential functions

Fortran 90/95 adaptation of a couple of EISPACK Fortran 77 routines

A PyGame simulation of the motion over time for an interconnected mass-spring-system with n masses

Linear Algebra Step by Step by Kuldeep Singh. Solved step by step through LaTeX

This MATLAB program is designed to calculate eigenvalues and eigenvectors of a square matrix provided by the user. Eigenvalues and eigenvectors are fundamental concepts in linear algebra and have various applications in mathematics, science, and engineering.

Course project designed as TA for Linear Algebra, utilizing Markov Chains to model economic dynamics.

Algoritmos de métodos numéricos, se pueden encontrar métodos como búsqueda de raíces, métodos de derivación, integración, métodos para encontrar eigenvalores, etc.

This program is implemented as a project for EE 242 course, and it implements Normalized Power Iteration with Deflation algorithmm to calculate most dominant eigenvalue, its eigenvector and the second most dominant eigenvalue.

This repository contains all the quizzes and assignments required to complete all 3 courses of the Mathematics for Machine Learning Specialization on Coursera

In this project, I've used College/University data to perform Principal Component Analysis and provide its implications to the business

DCGeig is a solver for large, sparse generalized eigenvalue problems with real symmetric positive definite matrices. It computes eigenvalues and eigenvectors and can be used, e.g., for computing eigenfrequencies of finite element models.