Multivariable Calculus

TL;DR:

I’ve uploaded the notes I took for MA 36200, Topics in Vector Calculus, to the course notes page.
- These were, unlike all the other notes I host, not written as I went to class, so they’re not really lecture notes.
The notes work as an acceptable in-between an actual textbook and actual lecture notes, but I warn that since they weren’t written as I went to class, you should ensure you’re looking at the right content.
Other resources I think people should look at when doing multivariable calculus:

The rest of this post is going to talk about my experience taking MA 36200, and how I ended up producing the notes.

Rewriting two posts

I started this blog as a freshman in college, wanting to jot down a, at the time, fascinating formula I derived to get around an asinine coding problem (which apparently, is still helpful to this day). I wrote multiple posts that essentially amounted to philosophy on how to be a good student/how not to be a good student, enchanted with the idea that I saw past the fog that no one else could.

I still think what I wrote then is true. Students, above all else, do a terrible job being honest with themselves. This goes in multiple directions, really. Poor professors and bad textbooks are just accepted without ever considering alternatives. Hundreds of textbooks are available online for any subject as well as the lecture notes and videos of other professors, yet go unused. Students also are not honest in what they do know and what they do not know, essentially wasting their time studying.

Okay. That’s the gist of it. Time to review some posts.

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CS18000 Fall 2019 Midterm 1 Reflections

This past Tuesday (October 1) I once again had the fortune to proctor the CS18000, the inaugural CS course, fall midterm 1. Unfortunately, I’m unable to provide the problems or solutions (or even my own solutions) but I figure some general discussion is okay. Any sort of incriminating or sensitive events will be left out.

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MA 35100 Course Notes

I have just uploaded by MA 35100 course notes to the handouts page. Contrary to the page name, this is not a handout (and not something I would physically hand out, ever). These are not polished or edited; I welcome any and all suggestions and corrections. As a credibility statement, I finished the course with an A+.

25 pages are handwritten and makes the document quite large. I apologize.

As a note to those reading these notes that are taking MA 26500, this does not cover all topics in the MA265 curriculum (orthogonal bases). There’s also more proofs than the MA265 curriculum as well.

Demystifying the computer/data science CODO process

In recent semesters, an increasingly large number of students aim to CODO — Change of Degree Objective, essentially switching majors — into the Department of Computer Science to varying degrees of success. As someone who has successfully CODO’d into the Department of Computer Science to major in data science as well as being in contact with students that have attempted/are attempting to/have successfully CODO’d (both friends and students I have TA’d for), I have discussed the CODO process countless times. Misinformation has certainly been spread. In this post, I will detail the CODO requirements and give some commentary on how to increase one’s chances.

My information comes from two official sources: the College of Science’s Academic Advising’s CODO Requirements for 2019-2020 and the Department of Computer Science’s CODO Requirements.

[EDIT 2020 April 13]: I’d like to mention that outside of parts that make direct use of official sources, much of this blog post was written with some guess work. While I wrote this after having had successfully CODO’d into data science, I know more now than I knew then. Unfortunately, the things I have since learned and confirmed are not things I can make public, sorry.

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On academic lesson structure

Months ago in March, I visited University of California: Davis, and attended a MATH 17C lecture with a friend. This course parallels (partially) the Purdue course MA 16020; both are essentially “calculus with random advanced topics, targeted at non-stem majors.” The lesson was on the dot product and its applications to vectors and geometry in $\mathbb{R}^n$ ; this was after the course had their one week crash course in linear algebra. Curricula aside, I found the lecture quite hard to follow. Having already learned the material, this difficulty was not from the topics, but the structure of the lecture. The lesson featured all the important (at least, what I consider important) parts of a lecture: motivation, derivation, explanation and examples, but the order made it impenetrable. There are undoubtedly many ways to poorly structure a lesson. Does there exist a correct way? I think so.

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CS18000 Summer 2019 Midterm Reflections

This past Monday (June 8) I had the fortune to proctor the CS18000, the inaugural CS course, summer midterm. Unfortunately, I’m unable to provide the problems or solutions (or even my own solutions) but I figure some general discussion is okay. Any sort of incriminating or sensitive events will be left out.

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Studying myths

[All-nighters] are romanticized.
Christopher Lin, 2019 February 26

People really like to gas up the idea of an all-nighter and other practices that entail putting life on the back-burner and absurd self-sacrifice for some noble goal. This could not be farther from the truth. But I don’t think this stops here, no, I think in terms of studying, people tend to have a lot of false beliefs, often to their own determent. I think part of the reason why I have (at least at time of writing) been successful in academic endeavors is that I have personally dispelled most of these myths. That’s not to say that these myths are inherently bad; many people are able to succeed while believing in said myths, and some people are able to succeed because of said perpetrated myths. But I think for most people this is not true. It’s probably better for them to know the reality of these things, too.

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Comparing CS18000 and CS15900

TL;DR: I think CS180 is a difficult but fair a class whereas CS159 is potentially easier but a lot more tricky.

But why?

Talent is (often) an excuse

Whether or not you think talent exists, if someone brings up talent, it’s an excuse for something else.

[EDIT 2020 April 13:] I wrote this on a whim and very quickly. I add this disclaimer not as means to avoid being held accountable for my opinions but because I find that my prose and explanations were, frankly, not the best. I’d like to revisit this topic later.

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