HomeCoursesComputing Matrix Algebra with R and Rcpp
4.8

Intermediate

19h

Updated 2 months ago

Computing Matrix Algebra with R and Rcpp

Explore matrix summation, multiplication, LU factorization, and eigendecomposition. Discover applications in machine learning, signal and image processing.
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Overview
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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

1.

Introduction

1 Lessons

Get familiar with practical matrix algebra computation using R, Rcpp, and C++ libraries.

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.

14.

Wrapping It Up

1 Lessons

Build on knowledge of matrix algebra and R for future learning opportunities.
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Developed by MAANG Engineers
Every Educative lesson is designed by our in-house team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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