Review of 'AI Programming with Python' (2024 Spring)

This course is the required course for the AI Convergence Advanced Major. 

The course seemed like a normal Python lecture, but the level was pretty high. I had to make Conway's game of life, complete a binary tree, and take a calculator applied class. 

In fact, during the course, I regretted it because it was too tough, considering that the course will be GE if I don't major in AI convergence advanced. However, after finishing the course, I felt that my programming capacity had grown, as Baekjoon's problems were solved well.

The textbook was 'Introduction to computer science using python; a computational problem-solving focus', written by Charles Dierbach. But I didn't use it. It was enough to read the lecture PPT.

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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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