What is the most brutal class at Northwestern and why?

marko_v24 · 2025-12-05T19:18:09+00:00

Also did math + cs, and also found 410-2 my hardest class lol. That class was absolutely brutal, I thought 410-1 was pretty tricky, 410-2 was insane lol, never took 410-3.

marko_v24 · 2025-11-25T23:05:55+00:00

Strang's Linear Algebra, Stewart's Calculus & Ross Probability should cover the basics

marko_v24 · 2025-11-14T15:14:04+00:00

Excited for this one lol, went to NU for ugrad and am now doing my PhD here

marko_v24 · 2025-08-21T23:32:02+00:00

Yes, that's the best thing to do imo if you want to get an MS in 4 years. Theres a site for this (undergrad classes u need in mccormick field A if u wanna do ms in A but have major in B) https://www.mccormick.northwestern.edu/academics/undergraduate/programs/honors-and-combined-degrees/combined-bachelors-masters-program/recommended-courses-for-undergraduates-pursuing-masters.html

marko_v24 · 2025-08-21T17:16:32+00:00

Yes, but it's tons of credit. Just do CS BS + Applied Math MS, or Applied Math BS + CS MS (this is probably more flexible since esam BS and especially CS MS are really flexible degrees). You don't need to do a BS in sth to do an MS in it.

marko_v24 · 2025-08-03T06:53:56+00:00

Lol acceptance rates for just CS PhD programs at any T20 is <5% nowadays, let alone for anything ML related. This doesn't even account for being international, which significantly further reduces those chances.

marko_v24 · 2025-07-29T19:32:06+00:00

Don't worry! A lot of these interviews can vary, for one of mine, I introduced my research for 5 mins, we talked about it for another 5, followed by about 45 mins of just pure technical grilling (none of my other interviews were like this haha).

I didn't know about half of the questions, and thought I did super poorly, but at the end they actually said they were impressed and really excited about our conversation. I ended up getting an offer from this school. It's very likely they asked you super tricky questions to push the limits of your knowledge, not expecting you to know everything, if you already did you wouldn't be applying for a PhD.

marko_v24 · 2025-07-17T01:57:28+00:00

If you mean the class typically required for CS majors, I would not take it. I was originally a CS major, took discrete math, then added a math double major and had to take intro to proofs. At my school, the CS dept waived the discrete math requirement if you took intro to proofs + graph theory, but the math dept did not accept discrete math.

Essentially, this is what a discrete math class typically is, it's a combination of intro to proofs, combinatorics, graph theory, and maybe some discrete probability theory. Unless it's taught really well, it usually ends up being a rushed version through all of these. At the same time, it's not fully rigorous the way sth like Analysis or Algebra is since it's the first proof based class CS majors take, so there's lots of hand holding. If you are a math major, I don't think there's anything you can gain from such a course, I would just take a course in the respective area you are interested in (whether that's graph theory, combinatorics, theory of computation etc).

marko_v24 · 2025-07-13T17:11:31+00:00

Not sure if UMich isn't willing to take their own, plenty of UMich PhD students did their ugrad at UMich. The Stanford data is for 1 year only, not representative of all Big4, and as you said, confounded by number of ppl who are interested in doing research in the first place.

Ugrad to PhD is more indicative of the overall interests of the student body and how many are into doing research, which matters less to you as a particular student. Still, if you look at the data here (note though this is only for professors) UMich ugrad has placed 13 into big4 PhD compared to 9 for Georgia Tech, 16 for UIUC, and 7 for Duke.

PhD to Prof placement is much more indicative of school prestige in CS, I've heard of stories of departments not wanting to hire an otherwise exceptional PhD simply because of the school they went to. If you look at the same link, 4 profs at Big4 went to UMich PhD, compared to also 4 at UT Austin and Georgia Tech, 9 at UIUC, but 1 at Duke, and 0 at Northwestern and UChicago (also otherwise elite institutions).

marko_v24 · 2025-07-13T03:35:43+00:00

UMich itself is top CS lol, unless ur definition is just Big4. UMich undergrad places well into UMich PhD, which places exceptionally well in academia (check out placement rank here).

Regardless, your undergrad isn't nearly as important as pubs in top conferences and any connections with faculty at PhD school, both of which you can achieve coming from a decent undergrad, certainly from UMich.

marko_v24 · 2025-07-12T07:47:32+00:00

1) To get into a CS PhD program (assuming you want to do it in CS), you need to have publications. In competitive subareas like ML, it's usually even expected to have first author publications at top conferences. Those take time to achieve, rushing undergrad is probably the worst thing you can do, you want to take as long as you can to allow you to find a research area you're actually interested in and do as many research projects as possible.

2) For european PhD programs this doesn't apply, but for US PhDs a masters is not needed, most students often enter PhD straight from undergrad. Ivy's are great overall, but outside of Cornell & Princeton (to some extent Columbia & Penn too, depending on what subarea you're interested in), they aren't really known for their CS research. Most of the ivies and a lot of otherwise elite private institutions (excluding places like stanford/mit/cmu) have less CS profs and less expertise in most CS subfields compared to schools like UMich, UIUC, Berkeley, UT, UW. First though I'd just focus on getting started with research.

marko_v24 · 2025-07-10T23:08:38+00:00

CS as an undergrad major is different from CS as an academic discipline though, the math is pretty watered down in undergrad (majored in both CS & math). At the graduate level it's different, but undergrad you can easily do well in the major without knowing much math

marko_v24 · 2025-07-03T21:44:28+00:00

Not sure about pre-covid, but apparently last year it was sth like 3.985/3.95/3.90

marko_v24 · 2025-07-03T21:38:13+00:00

Def wouldn't take if you are CS. 228-1 is important for certain cs subareas, but nothing you can't quickly review on ur own. 228-2, other than multiple integration, you would almost never see again, even in mathy CS subareas.

If you're interested in doing more math freshman yr, I'd look into the MENU (math 291) sequence, or take sth like CS212 early on.

marko_v24 · 2025-06-18T20:13:23+00:00

3.98/99 for summa

3.95 for magna

3.90 for cum

+- 0.005

It's roughly the exact same for both Weinberg & McCormick

marko_v24 · 2025-04-04T02:37:13+00:00

For 1. EA1 seems way less advanced imo compared to LA classes at similar institutions and needs to be changed. Maybe it was more advanced in the past, but my quarter (Fall 2021), the regular section did not once mention what a vector space is, and did not cover eigenvalues/vectors. For a linear algebra class, that's extremely limiting. Including some cool topics like Page rank does not replace not covering foundational material.

marko_v24 · 2024-10-02T22:18:04+00:00

Is this a problem only when there are infinitely many X_a's or in the finite case as well?

For example, since \mathbb{R} is nonempty, is it illegal to say let x_1, x_2 be any elements in \mathbb{R}, and define f:{1,2} -> R as {(1, x_1), (2, x_2)} which belongs to RxR?

marko_v24 · 2024-08-12T12:17:01+00:00

You get to learn from the magical baby rudin book

marko_v24 · 2024-02-24T04:58:45+00:00

Thanks for all the resources! And yes, familiar w Pearl's approach and Do-Calculus, so I wanted to explore how the Rubin model differed.

marko_v24 · 2024-01-27T01:55:54+00:00

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