Matrix algebra is the foundation for machine learning, signal processing, image processing, and many other popular algorithms. It is very important that aspiring computer scientists get a solid understanding of the subject, and there’s no better way to achieve this than coding matrix algebra operations.
The course aims to teach you how to code matrix algebra operations in R, and with main C++ matrix algebra libraries: RcppArmadillo and RcppEigen. Moreover, to clarify the matrix algebra operations, a simple algorithm is provided to understand the computing implications. The provided matrix algebra operations range from matrix summation and matrix multiplication to LU factorization and eigendecomposition.
The main course outcome is to develop hands-on experience via curated programs to perform the matrix algebra computation in the R and Rcpp ecosystem, also including the ability to develop algorithms for areas such as machine learning, image processing and signal processing.
Matrix algebra is the foundation for machine learning, signal processing, image processing, and many other popular algorithms. I...Show More
WHAT YOU'LL LEARN
An understanding of introductory matrix algebra
Working knowledge of coding matrix algebra operations in R
Ability to implement simple matrix algebra algorithm in Rcpp
Hands on experience of using RcppArmadillo and RcppEigen in R
An understanding of introductory matrix algebra
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Content
2.
Assignment Operator
3 Lessons
Get started with matrix assignments in R and Rcpp, plus a hands-on challenge.
3.
Addition of Matrices
3 Lessons
Break apart the principles of matrix addition and reinforce through quizzes and challenges.
4.
Scalar Multiplication of Matrices
3 Lessons
Build a foundation in scalar multiplication of matrices with R and Rcpp.
5.
Multiplication of Matrices
3 Lessons
Solve problems in matrix multiplication with R and Rcpp through practical exercises and quizzes.
6.
Transposition of Matrices
3 Lessons
Tackle transposing matrices in R and C++, assessing understanding, and checking symmetry.
7.
Determinant of a Matrix
3 Lessons
Practice using tools to calculate, interpret, and analyze matrix determinants efficiently.
8.
Inverse of a Matrix
3 Lessons
Learn how to use R to compute and verify matrix inversions effectively.
9.
System of Linear Equations
3 Lessons
Solve challenges with linear equations using R and C++, including quizzes and coding tasks.
10.
LU Matrix Factorization
3 Lessons
Go hands-on with LU matrix factorization to decompose matrices and efficiently solve linear systems.
11.
Cholesky Factorization
3 Lessons
Break down complex ideas of Cholesky factorization, tests, and practical challenges in R.
12.
QR Matrix Factorization
3 Lessons
Solve problems in QR factorization, validate with quizzes, and implement coding challenges.
13.
Eigendecomposition
3 Lessons
See how it works to decompose matrices into eigenvalues and eigenvectors using R and Rcpp.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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