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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.
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)
Project written in C++ on basic simulations of eigenstates of the Hamiltonian in the One Dimensional Schrödinger Equation for common potential functions
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 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.
This repository contains all the quizzes and assignments required to complete all 3 courses of the Mathematics for Machine Learning Specialization on Coursera
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.