Happy I found this video.. even though there's a Corona lockdown :D

It's coincidentally rainy outside 😂

Based on previous experiance, because it is rainy at your side, i predict that you were probably not happy 😔

@@a7md944 Bob was more likely not happy, we are the hidden state - whats the probability that the lockdown was not justified and that people were dying because of lack of medical help instead of the illness.

Eigenvectors and SVD for sure.

@@jacobmoore8734 check out 3blue1brown's channel for the Essence of Linear Algebra. He explains that matrices are linear functions like y=f(x) or like a line 'y=mx', with y-intercept b=0. Eigenvectors are special inputs 'x' such that f(x) = kx, where k is some scalar coefficient (k is the eigenvalue associated with the special input x). For certain types of NxN matrices, (the covariance matrix used in PCA for example) are super interesting because any point in N-dimensional coordinates can be represented as a linear combination (ax1 + bx2+...) of the eigenvectors. The eigenvectors form a 'basis' for that space. This is where SVD (singular value decomposition) comes in. SVD essentially asks "instead of just multiplying x by your matrix, why don't you decompose this task into 3 easier tasks?" Let's say your matrix is C for covariance. Then SVD says that C = ULU' where U is made up of the eigenvectors for columns, U' is the transpose of U, and L is a diagonal matrix with the eigenvalues. Pretend we're doing y = C*x. Then first we do w = U'*x. This essentially represents x as a linear combination of eigenvectors. Said another way, you've changed the representation of point x from the original coordinate system to the eigenvector coordinate system. Next we do z = L*w, which scales every value of vector w by an eigenvalue. Some of these eigenvalues are very small and the result in z is perhaps closer to 0. Some of these eigenvalues are relatively large and upscale the result in z. Finally, when you do y = U*z, all you're doing it translating your scaled z vector back into the original coordinate system. So SVD basically splits a matrix into 3 different operations: 1. represents an input vector in terms of eigenvector coordinates 2. scales each coordinate by an eigenvalue 3. represents the scaled result back in terms of the original coordinates When you look at PCA (principal components analysis), you take your covariance matrix and decompose it to look at how much your eigenvalues scale the eigenvector coordinates. The largest eigenvalues correspond to the direction (eigenvector) of largest variation

Definitely eigenvectors! Please!

Yes, please, do that.

You may have already found some, this is an attempt by University of Calcutta, not so coolly done, but please see if it makes sense kzfaq.info/get/bejne/eZyWe5h_y9XOZGQ.html

Isn't this opposite of calculus? Discrete vs continuous functions.

Thank you Changyu!

Thank you for your note, I hope the midterm went great! :)

Similar situation here, I have a master thesis in anomaly detection, and using HMM is a candidate. I'm afraid it's much more complicated than this, but it sure made it look less scary