Principal Component Analysis (PCA)

Principal component analysis (PCA) is a workhorse algorithm in statistics, where dominant correlation patterns are extracted from high-dimensional data.

These lectures follow Chapter 1 from: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz

This video was produced at the University of Washington

[TlnITA]

The best video on PCA I could find on youtube, no messy blackboards, jokes or oversimplification, just solid explanation, great job.

[rezab]

What the hell. One can download your book for free?! You sir are a saint. I will work thru it and if I like it I will definitely purchase it!! (I'm pretty sure I will like it, because I like all your videos so far)

PS: I am so proud of you guys. You are bringing humanity forward with content like this being free. I encourage everyone who can to purchase content from sources like this

[sjh]

Prof. Brunton always delivers the best explanations on the subjects! His videos really help me a lot! Kudos!

[amisteiner]

You explain complicated math in a brilliant way. Thank you so much

[aayushpatel]

These videos are the PCA for data driven engineering!!Thank you for bringing up these series publicly!!

[GreenCreepLoL]

I logged in just for this, which I almost never do xD

I wanted to say: Thank you!
Your video series is great, enjoyful, and helps getting familiar with the topic rapidly. The same applies to the book, which you link at for free. Thank you.

[vijanth]

Steve, able to explain PCA from classical statistiscal point of view. Very clear

[danielzhang]

So far this is the best video of PCA explanation.

[vijanth]

The alst part of the video on how SVD and PCA are related really class of its own. IT show the expert should run video lectures

[sheethouse]

Finally, someone who explains statistics in a straight-forward way, whilst communicating in an adult like manner.

[shashidharmuniswamy]

Thank you, Prof. Brunton. I have a question: supposing I have done this series of experiments with a target measure that cannot be categorized but is a continuous value, then can I use PCA?

[resap.]

I've watched a lot of PCA videos and this is really the best one. You're amazing!

[richardlin]

Best explanation. Looking forward to video about Kernel PCA！

[lucasheterjag]

Love this series! Just bought your book

[rodrigomaximo]

Amazing video. I did the MIT lectures about Linear Algebra (that talked about SVD) and the Andrew Ng's ML course (that talked about PCA). This video was the perfect bridge to connect the two things in a coherent manner. Thank you very much, Dr. Brunton!

[jaanuskiipli]

Correct me if I'm wrong, but B transposed multiplied by B sums up the products of mean centered values, but to get the covariation we still need to divide by number of rows in X as covariation is defined as
E{(X-E(X))*(Y-E(Y))} not just sum of (X-E(X))*(Y-E(Y)) over measurements

[pbpemkc]

I am a phd student learning inverse scattering, your lectures help me with understanding those concept :) greetings from naples

[hectorponce]

Does the concept of cross-loadings exist in PCA like it does in EFA? If it does exist, what are the criteria to determine so?

[criticalcog]

Wow, excellent explanation. Thank you so much.

[jacobanderson]

Do you have a patreon? How can I help support this content? Just these materials on Ch1 and 2 have been amazing. Will it extend to addiitonal chapters?