Lecture 8: Lebesgue Measurable Subsets and Measure

[@hausdorffm]

I really like this lecture! I went over what I learnt in lectures long ago. 43:45 It is boring thing, but the third equality in the estimate of m(A_1) + m(A_2) at 43:45 is not correct or not necessary. Since length(I_n) = length(J_n) + length(K_n) and the definition of I_n, we should omit the third equality from the estimation of m(A_1) + m(A_2).

[@carl3260]

Not sure this is right, the chain seems correct: measure of A_1,A_2 ---> measure of J,K ---> length of J,K ---> length of Is ---> measure of A (but, the epsilon at 44:06 shouldn't be there and Is should be defined at the start such that their union contains A)

[@thiennhatvuong4055]

@MITOCW can you up the course Real Analysis 18.100B?

[@KrazyKittyTailz]

What exactly is the difference between 18.100A (which I've taken) and 18.100B? Seems that MIT subsections quite a lot of its math courses!

[@sunritroykarmakar4406]

Amazing lecture . Really solid

[@joenissan]

What would any if this be used for?

[@爸爸到底-s9x]

Probability theory.

[@joenissan]

@@爸爸到底-s9x Lol….theory of what

[@爸爸到底-s9x]

@@joenissan Like I said, theory of probability.

[@anatolmelaku2585]

@@joenissan Wall street.

[@jonathanma9217]

I see you on most of these functional analysis lectures. Are you trolling? Or have you genuinely found a way to stumble into this obscure but important branch of mathematics?