Review of Linear Algebra in terms of application (2024 Fall)

 Usually, linear algebra courses are discerned by two POVs. One starts from linear equation systems, and the other one starts from vector space. The course I took last semester(2024 Fall) was the former, with the book 'Contemporary Linear Algebra' written by Howard Anton et al.

I learned linear equation systems, RREF, determinant, rank, nullity, pivot theorem, and so on in the mid-term exam extent. I also learned eigen-, projection theorem, least square theorem, Gram-Schmidt process, orthogonality and orthonormality, orthogonal matrix, SVD, LU/QR decomposition, quadratic form, and some methods for optimization, and so on.

The course was very nice, and I'm especially thankful for the MIT Online course, as I got much help from Strang's lecture.

However, as with any lecture handling matrix, it was hard to learn rigorous proofs. So I'm eagerly feeling the need to take the course with many proofs. I've made up my mind to take the course next semester.



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Paper review: migration? just studying?

 International Student Mobility: Growth and Dispersion by Neeraj Kaushal & Mauro Lanati, 2019. NBER Working Papers Personally, I found the paper difficult to read. This was because the paper didn't use identification strategy such as IV and DiD. Also, the main purpose of this paper was not to identify rigorous causal effects. Rather, the author tried to assess which of two competing models was more plausible. The paper felt unfamiliar to me, as well as theoretically heavy in its derivation of the regression equations. The process to connect the theory to the variable choice was a little bit uncomfortable for me. But, very fun! If I do my own research, I think I can refer the way the paper constructs regression equations. The paper proposes two competitive models. One is 'migration model', representing permanent migration through the tertiary education, and the other one is the 'human capital model', representing  skills acquisition through studying abroad. The p...
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Review of 'Introduction to Statistics' (2024 Spring)

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My university set all of the students majoring in economics to take this course. This course deals with not only basic stats knowledge like conditional probability, binomial/discrete/continuous distribution, and Poisson / exponential distribution but also hypothesis tests, like fundamental concepts of the test, t-test, paired t-test, variance estimation using Chi-square distribution, and F-distribution. Unfortunately, the opportunity to conduct real statistical analysis as it was a very large lecture. But the contents were pretty dense. So it was very helpful when I studied linear regression and ANOVA myself.
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BITAmin Winter Session 3

I tried to understand the CNN in terms of math, but it was not easy to organize its concept explicitly, especially because of the concept 'convolution'. However, thankfully I could seize its intuitive understanding with 3B1B's video . Attention was pretty not easy to analyze mathematically, so I had to skip the math-hard comprehension. But still Hyukpenheim's video  is surprisingly useful. So I only organized the notes about RNN and LSTM. Download PDF
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