In this chapter, you will learn about the key objects in linear algebra, such as vectors and matrices. You will understand why they are important and how they interact with each other.

Motivations

Creating Vectors in R

The Algebra of Vectors

Creating Matrices in R

Matrix-Vector Operations

Matrix-Vector Compatibility

Matrix Multiplication as a Transformation

Reflections

Matrix-Matrix Calculations

Matrix Multiplication Compatibility

Matrix Multiplication - Order Matters

Intro to The Matrix Inverse

Introduction to Linear Algebra

Many machine learning algorithms boil down to solving a matrix-vector equation.  In this chapter, you learn what matrix-vector equations are trying to accomplish and how to solve them in R.

Motivation for Solving Matrix-Vector Equations

The Meaning of Ax = b

Exploring WNBA Data

Matrix-Vector Equations - Some Theory

Why is a Matrix Not Invertible?

Understanding a Linear System's Three Outcomes

Understanding the Massey Matrix

Adjusting the Massey Matrix

Inverting the Massey Matrix

Solving Matrix-Vector Equations

An Analogy with Regular Algebra

2017 WNBA Ratings!

Who Was the Champion?

Other Considerations for Matrix-Vector Equations

Other Methods for Matrix-Vector Equations

Alternatives to the Regular Matrix Inverse

Matrix-Vector Equations

Matrix operations are complex. Eigenvalue/eigenvector analyses allow you
to decompose these operations into simpler ones for the sake of image recognition, genomic analysis, and more!

Introductie tot eigenwaarden en eigenvectoren

Scalair vermenigvuldigen interpreteren

Schalen langs verschillende assen

Definitie van eigenwaarden en eigenvectoren

Waarom "Eigen"?

Eigenwaarden vinden in R

Scalair veelvouden van eigenvectoren zijn eigenvectoren

Eigenwaarden en eigenvectoren berekenen in R

Hoeveel eigenwaarden?

De wiskunde achter eigenwaarden verifiëren

Eigenvectoren berekenen in R

Nog wat meer over eigenwaarden en eigenvectoren

Ordening van eigenwaarden

Markov-modellen voor allelfrequenties

Eigenvalues and Eigenvectors

“Big Data” is ubiquitous in data science and its applications.  However, redundancy in these datasets can be problematic.  In this chapter, we learn about principal component analysis and how it can be used in dimension reduction.

Intro to the Idea of PCA

What Does "Big Data" Mean?

Finding Redundancies

The Linear Algebra Behind PCA

Covariance Explored

Standardizing Your Data

Variance/Covariance Calculations

Eigenanalyses of Combine Data

Where's the Variance?

Performing PCA in R

Scaling Data Before PCA

Summarizing PCA in R

Does Subsetting Change Things?

Wrap-Up

Principal Component Analysis

NFL Player dataset

WNBA Massey Matrix dataset

WNBA Point Differentials dataset

Lineaire algebra is een van de belangrijkste hulpmiddellensets in de toegepaste wiskunde en data science. In deze cursus leer je werken met vectoren en matrices, matrix-vectorvergelijkingen oplossen, analyses met eigenwaarden en eigenvectoren uitvoeren en principal component analysis gebruiken om dimensiereductie toe te passen op realistische gegevenssets. Alle analyses worden uitgevoerd in R, een van de populairste programmeertalen ter wereld.

Introduction to R

Deze cursus datawetenschap introduceert lineaire algebra, matrix-vectorvergelijkingen en PCA.

Lineaire algebra voor data science in R

Deze cursus is een inleiding tot lineaire algebra, een van de belangrijkste wiskundige onderwerpen die ten grondslag liggen aan datawetenschap.


Waarom "Eigen"?

Lineaire algebra voor data science in R

Praktische interactieve oefening