6.5, due on Nov 12

Difficult:
I could definitely use an explanation why we would choose a Beta distribution as our prior for a Bernoulli distribution. What determines what distribution we use as a prior? Do we have to choose a distribution as a prior?

Reflective:
I feel like I have been able to do the problems in this chapter because I knew the section that they pertained to. But if I were to be given a fresh problem, I wouldn't necessarily know what tools to apply to it. Would it be possible in our review session in class on Wednesday to go through a list of problems and figure out which theorems and known distributions to apply to those problems?

Comments

Post a Comment

Popular posts from this blog

8.7, due December 6

Difficult: The hard part of this section for me was understanding formula 8.48 and the Vector Space Structure section -- specifically the part about scaling relations. Reflective: I can see how these kinds of functions could be better for approximating functions with local behavior, whereas to express such functions in a Fourier basis would be very computationally expensive. I can also see how the simplicity of the function could cause it to be easier to work with (differentiate, evaluate, etc.)
Read more

8.5, due Dec 3

Difficult: I understood the mathematical concept of a convolution. And I understand how convolutional neural networks work like the back of my hand. But after reading this section, I still don't understand how the two mesh together. I was on board with the description given in the intro to 8.5, until they barely mentioned filters and feature extraction again for the rest of the section. Ex. 8.5.11 helped, but more examples like that in class would make a big difference. Reflective: I enjoyed what I did understand of Ex. 8.5.11, which explained that you could apply a convolution to a set of samples to remove some frequencies while leaving others untouched. I still missed the "How", though.
Read more

8.6, due Dec 5

Difficult: This section was actually pretty reasonable. The only part I had trouble with was understanding their method of antialiasing. I don't understand exactly how you can remove all Fourier coefficients outside a certain interval. Reflective: I really liked their examples of aliasing -- the spinning rotor blades, and even the plotting of functions. That was creative but very applicable example.
Read more