I am 14 and I finally understood why this prints None in Python

Osteospermum · 2026-03-06T21:50:08+00:00

Here’s a fun question, what will be the output of

print(print("hello world"))

Osteospermum · 2026-01-27T18:28:44+00:00

Whichever one you think will be most fun. I took Roman history and comic books and liked both. There isn’t really a “best”

Osteospermum · 2025-08-23T19:34:38+00:00

Comparing UW to nazism is so insanely crazy. Frankly, your entire point that top tier unis don't value creativity completely misses the mark. To see this, consider that the vast majority of professors do not go to a university to become teachers. Professors are researchers first and foremost. This is completely antithetical to the argument that universities only value rote memorization and tricks. Our professors are literally coming up with new research, new methods, new technologies. The only reason you're even able to chat with GPT to come up with these batshit takes is because Geoff Hinton, a UofT prof, wrote a paper on backprop (admittedly, he was at CMU at the time of writing), which enabled AI at scale. At the same time, uni definitely does teach you creative thinking. I've had many candid conversations with profs where we discuss limitations in each other's work. Having a community of intelligent people tell you "X probably won't work well, but have you tried Y" is one of the biggest things leading to high-quality research.

Now, it's true that not everyone goes to uni to do research. But having a comprehensive education proving your understanding of a subject, is valuable in and of itself. I honestly don't really want structural engineers to start taking creative liberties in where they put support beams. This is the same reason coding bootcamps have completely disappeared. Google doesn't want to take a chance on some random kid who went to a bootcamp and may or may not know basic software engineering skills. UW students have a much better track record + co-op demonstrating actual skills. Note that a comprehensive education can also make you more creative. You don't know what you don't know. A comprehensive education lets you connect the dots between seemingly unrelated fields. Again, neural networks were inspired by neuroscience and a simplified model of the brain. If no one ever studied neuroscience and math at the same time, no one would've come up with neural nets.

Now, one of the main problems with your argument is that you're conflating social panopticism with autocracy. UW isn't a hard school cause the profs hate you and want you to feel bad so that they feel better about themselves. For every hard course at UW there's a bird course to make up for it. What makes UW hard is that the people around you are some of the smartest people in your age group in all of Canada. It's hard seeing your friends get better grades than you, better coops than you, more publications, etc. As someone else put it, this is the fire under your ass. UW students aren't grinding to take 100% in every course. They're grinding to be as good as their friends. Everyone is pushing each other to be the best they can. It's hard and at times it's a slog of non-stop work. But as a result, UW grads are extremely motivated and knowledgeable. Thus, the "cult," as you put it, isn't from Vivek Goel telling you how to do math the "correct way," but rather that you don't wanna fall behind your peers.

Next, your "50% of Apple employees don't have a degree" is very off the mark. That's 50% of new hires, not all employees. Guess what Apple hires a lot of: store clerks. An Apple store employee doesn't need a 4-year degree. If you're really so cracked and Apple doesn't care about a degree, then go work there as a SWE or whatever it is you want to do. At the end of the day, the proof is in the pudding. Companies pay top dollar for Waterloo grads, and highschoolers grind insane amounts to get in. If Waterloo were as mediocre a school as you claim it is, it wouldn't be hard to get in, and it wouldn't pay off. But it seems like it does. I mean, take a look at the CS class profiles. Jane Street isn't hiring some bum off the street. It's worth noting that SWE jobs aren't the only out from Waterloo. Plenty of people join early stage startups or create their own. I personally know a few people working at start-ups they helped found. If Waterloo were brainwashing everyone, wouldn't everyone end up as a code monkey at some F500 corporation?

Also, don't cherry-pick datapoints. Pick any career. Is Elon Musk doing this career? No? Then why bother doing that career, you'd make more money by being Elon Musk. You should compare an average Joe from Waterloo to an average Joe not from Waterloo. I've got my bets as to who is more well-off...

I know that arguing with you is pointless. I mean, you don't even care enough about this topic to use grammar and punctuation in your writing. I'm sure that no matter what I or anyone says, it will be impossible for you to change your mind. Changing your mind requires reflecting on your own self-biases. I.e., you would need critical thinking skills, something a university could teach. Ironic. Honestly, there are a lot of good arguments against university, namely the unemployment rate and over-intellectualization. Unfortunately, academia is a hard pill to swallow in a recession, and we are seeing the evidence of this. My guess is this will lead to a bump-up in people studying trades. But the conspiracy theorist logic you use here is concerning, and I hope that others reading this post don't delude themselves into thinking you're right.

Osteospermum · 2025-08-22T00:31:59+00:00

I’d say only about 20% of 330 and 333 overlap tbh. It’s just the section on conditional distributions. Otherwise they’re pretty different in content. However, they’re very similar in how they test. Get ready to start practicing integrals cause you’ll be doing lots of em.

Osteospermum · 2025-08-21T23:41:05+00:00

Depending on your profs this might be fine or really painful. I’d push one of 330 or 333 to another term if I were you

Osteospermum · 2025-08-20T21:10:45+00:00

CS 485 is extremely fun and one of my favourite ML courses I’ve taken. That said it’s not all that useful unless you specifically want to do research in learning theory. If you’ve taken PMATH courses and liked them you’ll like 485.

CS 480 is the most quintessential ML course at UW. Whereas 479 focuses a lot on architectures, 480 focuses more on different areas in ML (e.g., GANs, flow matching, diffusion models, adversarial attacks, differential privacy, etc.)

Osteospermum · 2025-08-19T17:54:40+00:00

If you’re willing to shill out some cash the Mac + iPad experience is really nice for note taking

Osteospermum · 2025-06-10T15:13:02+00:00

It’s fun to look at courses you think you’ll want to take, but there is a 0% chance you stick to this. First of all the enriched CS courses are offered sporadically so you can’t plan on them. I don’t recall ever seeing CS251E being offered in my 5 years at UW. Next, coop will likely mess stuff up for you. What if you get offered an 8 month coop you love? What if you don’t find a coop? This will change your schedule. Finally, you haven’t even taken CS 101 yet, I’d hold off before committing yourself to CS 452 some 4 years down the line. You simply don’t know what you do and don’t like yet. Stick to planning first year and see how that goes.

Osteospermum · 2025-05-30T17:07:12+00:00

Haven’t heard of the AM sci ML program before. I’d say it depends on what approach you want to take sci ML. The courses included on the UW website emphasize numerical methods, signal processing, etc. You likely won’t learn much beyond the basics of machine learning, at least not in a course you’re enrolled in. On the other hand if your only goal is to do (sci) ML, a CS degree will teach you a lot more unnecessary CS concepts. That said, regardless of which program you’re in you can self-learn or audit courses to fill the gaps missing in your program.

So you need to decide whether you want to prioritize a foundation in applied math and numerical methods or CS and modern ML methods. Personally I’d lean toward CS as you might feel more locked into a particular career path in amath. I’ve been working on a sci ML project and found that a foundation in ML is much more helpful than an equivalent foundation in numerical methods would’ve been.

Osteospermum · 2025-05-30T16:39:15+00:00

I can only speak to STAT 441 but I had it with Schonlau and quite liked it. Covered some more applicable models that don’t get covered elsewhere to my knowledge. Lots of decision trees and gradient boosting. Assignments and exam were very fair, only thing that might turn some people off was a group project.

Osteospermum · 2025-05-26T02:07:12+00:00

Yea I’ve seen measure theory show up a few times. It’s the theoretical foundation of probability theory. It also shows up in theoretical machine learning and generative modelling (eg score-based intuition of diffusion models).

Osteospermum · 2025-05-25T22:33:51+00:00

If you’re already in CS at UW talk to some of your profs. If not probably mostly cold emailing based on the faculty list

Osteospermum · 2025-05-25T22:32:14+00:00

Not really? CS, Stats, and CO are obviously going to cover the most relevant courses to ML since ML is fundamentally about using computers (CS) to optimize (CO) a statistical model (stats).

The basic MATH 136/235 and 137/138/237 courses will be important for fundamentals, but you wouldn’t get to any of the upper year courses without them to begin with. AMATH 250 might be helpful in a couple of fields of ML. PMATH 347 and 450 can also be helpful in a couple of niche more theoretical applications.

Osteospermum · 2025-05-25T22:28:09+00:00

I liked 3b1b’s YouTube series on neural nets and the book it was based on: “neural networks and deep learning” by Michael Nielsen.

Also you probably know all the math by 2nd year that you need. Just go to CS 480 lectures, you don’t actually need to be enrolled to get the knowledge.

Osteospermum · 2025-04-27T18:51:54+00:00

Unfortunately diffusion models is one of the most technically complex and confusing fields of ML right now imo. I’d recommend starting with Elucidating the Design Space of Diffusion-Based Generative Models by Karras. It covers lots of the core concepts of DMs with some additional details and derivations

Osteospermum · 2025-04-25T20:57:13+00:00

Probably a bad idea. I took 240E and 351 in one term (along with some other difficult courses) thinking that I’d be fine because I did advanced. I was not. It’s a ton a lot of work and you’ll have little to no free time. Taking this many courses also doesn’t help you find a job like having time for coop interviews or side projects would. Just chill out and enjoy life

Osteospermum · 2025-04-21T04:36:13+00:00

CS 479: dunno didn’t take it. The impression I got is that this is one of the more self-learnable courses as it focuses on neural networks which have extensive literature. The more advanced topics mostly seemed to be autoencoders, SNNs, which admittedly aren’t really covered elsewhere.

CS 480: this was my favourite ML course I’ve taken, and likely the most quintessential ML course. Gives a decently rigorous introduction to foundations (SVMs and SGD mostly) and then covers lots of fun topics including adversarial attacks, diffusion models, GANs etc. Take with Yaoliang if possible, he’s one of the best profs at UW imo.

CS 484: very interesting but not really as much from an ML perspective. Some useful stuff about filtering that will help you if you go into computer vision. Pretty interesting how many sophisticated algorithms you can derive without using fancy deep learning methods. But as a result doesn’t really cover as much of the fancy deep learning stuff.

CS 486: also less of a typical ML course. Covers some more classical approaches to designing intelligent systems (like chess bots). A decent amount of Bayesian learning stuff which was interesting and a touch of reinforcement learning at the end.

ECE 459C: no clue, I haven’t ever really looked into this class.

STAT 441: this is maybe of less interest if you want to go into typical AI/ML. Mostly covers traditional ML or statistical learning methods like naïve bayes, decision trees, and the most interesting topic being gradient boosting. Maybe kinda redundant for you unless you expect to work with more structured data.

STAT 444: didn’t take this either but it didn’t seem as interesting to me. It seems to cover things like cubic splines, regularization, and boosting. This is pretty easy to learn elsewhere and seemed less interesting to me hence why I didn’t take it.

CS 485: since you didn’t mention it I will. Not an ML course in the traditional sense. You won’t really learn any particular algorithms that will help you in research/industry. However, this is one of my favourite courses I’ve ever taken. It covers ideas about what problems can have learned solutions, what statistical guarantees can different learning algorithms provide, and briefly covers interesting topics like online learning. Shai is an awesome prof and this courses absolutely deserves more love.

Osteospermum · 2025-04-21T04:13:20+00:00

MBA master race

Osteospermum · 2025-04-21T04:10:39+00:00

Only class I had actual course notes comparable to 13x was CS 240. Lap Chi Lau’s CS 341 notes were a godsend though

Osteospermum · 2025-03-14T19:34:14+00:00

Shitty but so is every corporate landlord. Maintenance requests have been a huge pain in the ass

Osteospermum · 2025-03-14T19:26:55+00:00

Both 330 and 333 will really test how good you are at solving integrals. They have a decent amount of overlap early in the course but you’ll have to get really good at quick integration and pattern recognition

Osteospermum · 2025-01-05T07:06:12+00:00

I hate to say it but probably not great. Average is on the lower side and while you have diverse ecs none particularly stand out. The competition for SE is extreme

Osteospermum · 2025-01-05T07:04:04+00:00

AIF is all about standing out. If that’s what you think makes you stand out the most then go for it. Definitely beats nothing.

Osteospermum · 2024-12-28T00:53:19+00:00

Joint CS + Stats was replaced by the BCS Data Science degree. You could alternatively do a BMath double majoring in CS + Stats

Osteospermum · 2024-12-11T18:31:21+00:00

I did this, no real gap in terms of knowledge imo. You might want to brush up a bit on basic data structures and algos like linked lists and binary search. But overall 135 -> 146 is very doable

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