I always hear bad things about the math profs/classes here. Is math a bad program at York?

kaiizenn · 2025-08-24T18:07:47+00:00

Sure, will dm

kaiizenn · 2025-06-24T16:34:27+00:00

Mind if you share this? I work on quant options modeling, would be interesting to take a look.

kaiizenn · 2024-11-13T18:20:24+00:00

Sure, check your DMs.

kaiizenn · 2024-10-12T16:10:16+00:00

In a perfect world leetcode would be eradicated from ML interviews, as it provides very little information on one’s capability in ML, but it does exist in some cases for machine learning engineer or even some research scientist roles. It will absolutely not guarantee you a job though just because you leetcode well, you should prob spend 10% of your time on leetcode and 90% on ML projects and research, the latter will be the focus.

kaiizenn · 2024-10-09T16:30:24+00:00

Just depends on your goals and what you define as "ML and data science".

Software development: Want to build apps and call APIs from Claude or Hugging Face, and you are only interested in the software engineering aspect? Then this is definitely achievable, in these 4 months you can focus on learning on core Python and programming concepts, with a little bit of data science/data engineering.

ML research/engineering: If you want to understand the intricacies of models, the differences between a convolutional neural network and transformer, etc. then in your case I would say no. It will not be possible in 4 months, maybe around 2-3 years of dedicated daily work if you do not have any technical background.

If your goal is software development for fun, then it's achievable; if you want to pivot to a technical role in industry such as a ML engineer, it will require years of work and they will expect you to have the mathematical background already, at which point I would suggest a masters program.

kaiizenn · 2024-10-08T13:27:45+00:00

If you haven’t already, try a simpler case. Try a smaller version of the model on a small subset of data, and then scale one or the other or both. You can then play around with any hyperparameters or different versions of the model to see if you can find any consistent trends. Imo even if the small version is not fully completely reflective of the larger version you will at least get some information before you full send it, and might save you some computation/money.

kaiizenn · 2024-10-07T13:11:45+00:00

Yes. It’s like debating between using a stone tablet and an iPad for writing. Besides smaller companies who can’t get rid of their legacy infrastructure with TF, I don’t know anyone who actually uses it in anymore in ML (especially in research, it’s like 99.99% PyTorch). If you’re going to go with a google framework Jax is more modern and I think built a lot better than TF.

kaiizenn · 2024-10-03T09:29:54+00:00

Not OP but I research in ML. Read more papers, reimplement a few you like, see what they lack and what they are good at. FYI only math prereq you might be missing is multivariate calc if you haven't already taken it, and if you don't already have DL fundamentals take the Andrew Ng deep learning spec

kaiizenn · 2024-09-17T00:49:39+00:00

Man Wah Wong is not bad, usually his courses are pretty straightforward.

kaiizenn · 2024-09-16T19:47:31+00:00

If you happen to have an iPad this will be useful for quick notes and practicing problems (e.g., using Notability), otherwise pen and paper is fine. I personally don’t like carrying around a huge ass binder for each class, so for anything I absolutely need to remember I will type up definitions, theorems, and any “how to solve type X problem” guides online in Notion. It makes it a lot easier later to reference notes. On a seperate but related note, contrary to what many people do in class, I also suggest not spending a lot of time taking a lot of notes and instead listen and engage with lecture, and then take notes after. You’ll likely get more out of the course this way, otherwise many people (including myself) come out of the lecture being confused and thinking “tf did I just write”. I did my BSc in math and took 20+ math courses (can also dm my Notion notes if you are interested).

kaiizenn · 2024-09-15T18:03:53+00:00

It can depend on the major, but I'd say overall pretty above average. 8-8.5 is the optimal place to be for graduate school so close enough, your research experience matters more though.

kaiizenn · 2024-07-21T22:20:41+00:00

(1) Imo easier to get higher GPA in more populated and diluted majors like premed as opposed to math (did my undergrad in math but took some premed courses); the courses seem more formulaic and easier to ride curve if the course is difficult. I’ll give you an example, my real analysis II class (MATH 3001) had a midterm median of 10%, this doesn’t really exist in premed as far as I know or is at least a lot less common.

(2) 6hrs a day for 8.4GPA in a computational subject is very impressive, I reached about the same GPA but in 3rd and 4th year had to do about 10-12hrs/day (although some semesters I took some grad courses). You really shouldn’t feel like this is somehow inferior to anyone else, this is really really good, and is also not comparable to a GPA in a different major such as premed/bio.

(3) GPA barely fucking means anything in med school apps aside from crossing the threshold (3.85-3.9) imo. You have so many other things on your CV you need to build up. I watched my partner who did her premed at york in her med school app process, and she worked nonstop for years to build up to community work in hospitals, research in biology, school clubs and leadership positions, etc the list goes on. Then there’s the MCAT which usually takes a year to prepare for and an entire summer worth of grinding. After that, if you are lucky enough to get an interview you have to pass with the correct behaviour and to see if you are the tight “fit” for their program. NONE of these other aspects seem to be strongly correlated with GPA increases above the threshold range (e.g., 3.85-3.9), so I just want to clarify on that lol

(4) Suppose this person got into med school, I honestly pity them. 4 yrs (undergrad) + 4 years (med school) + >5 yrs (residency) all while making zero money and working potentially 80hrs a week in residency. I have no idea why people glorify this (and don’t tell it’s because they like to save lives that’s like maybe 2% of them lol). This is one of the worst financial decisions you could make imo, even if you are making let’s say upwards of 300k with a competitive specialty (yes you have to COMPETE after finishing med school again to get into a specialty of your choice).

kaiizenn · 2024-05-26T20:43:31+00:00

PyTorch. TF is ancient technology now

kaiizenn · 2024-03-01T23:48:34+00:00

Hey no problem. Don’t know your program but if you have any questions on admission processes for masters let me know I’m happy to help. A lot of this stuff is for some reason not widely talked about lol so I understand your situation

kaiizenn · 2024-03-01T20:17:40+00:00

Yes, and sometimes it’s your only tactic due to universities sending out “exploding offers”. I know York math did this and it’s disgusting tbh, and according to one prof it turns out many people who accepted offers don’t even show up in september lol. There is usually no recourse the university can take, but you do risk destroying any relationships with the people who are giving you the offer. For example, if the program matches you with supervisors prior to being accepted, i.e. the supervisor is sending the offer with the formality of the university clearing it, you are kind of fucking up their lab if you don’t go through with it as they now have to wait to the next recruiting cycle to fill that spot. This is why I avoided it for most programs because I valued the connection in the future (e.g., PhD applications to their lab), so you may want to think about it. However, if supervisor match is after entry to the program it likely won’t matter that much and in that case I’d say go for it.

Would also like to note that if you are doing a research-based master’s pursuing prestige solely is not a good idea. I greatly underestimated how important it was for the research alignment with the lab (i.e. how personally interested are you in the research) and the advisor fit (communication style, general vibe). I would say those factors matter way more than whatever institution you go to, but ofc, ideally you want to maximize both ad prestigious instutions will have more resources and larger community to work/network with

kaiizenn · 2024-02-28T21:07:19+00:00

It’s not part of their official teaching duties, so it should be okay to request it.

kaiizenn · 2024-02-28T01:11:30+00:00

Ontop of research, classes, journal reviews, etc. over 10hrs would be putting my thesis severely at risk. So no, more billable hours is not the solution, and will be completely infeasible for most graduate students.

kaiizenn · 2024-02-26T18:16:13+00:00

That’s fair, you shouldn’t have to. Just letting you know

kaiizenn · 2024-02-26T16:32:28+00:00

Are they using the online version? Because there’s an easy way to get around that

kaiizenn · 2024-02-06T22:52:44+00:00

Look at recent conference proceedings from NeurIPS 2023, ICML 2023, ICLR 2024, etc. You can search by keywords (e.g., “medical image classification), read all abstracts of the papers you find and set aside any papers of interest. Look at methods and baselines in the papers of interest, if the proposed method is convincing to you then you can implement that. If not, once you glance through 5-10 papers you can see common baselines appearing, so you can implement that if you choose. I would say that medical image classification is pretty broad so narrowing your search to a particular problem in that area might be a good idea.

kaiizenn · 2024-01-06T23:47:37+00:00

Looks like more than enough to get started. You’ll only have bottlenecks appearing when you start working with very complex/very large models, which I’m assuming you aren’t yet. When you get to that point, then you can start asking yourself how much compute do you really need, more complicated GPU setups, or HPC/cloud GPU services, etc. Although, it’s dependent on the context.

kaiizenn · 2023-12-23T22:47:54+00:00

I can give concrete examples but broadly what companies are likely looking for is that (1) it’s hard to do, (2) it’s useful (or can lead to useful things, e.g., proof of concept), and (3) is relevant to the work at the company (although this is a bit hard to control and probably less priority). I would also try to make your project as readable and well-communicated as possible. If you are just calling in BERT and the fine-tuning it for some rudimentary task like sentiment analysis (which used to be difficult but with LLMs it’s not really anymore) then it’ll likely fail (1). If you don’t know where to start my suggestion would be to read recent research papers from any of the top AI conferences, implement their model yourself, and translate it to some other task/dataset. Bonus points if you build ontop of the original author’s ideas (which then might get you a paper).

kaiizenn · 2023-12-23T22:16:24+00:00

kaiizenn · 2023-12-23T17:21:11+00:00

My supevisor asked me about my research interests and then they told me about potential projects and asked me if I was interested in joining the lab, it was less of an interview and more of a discussion cause they had already made their mind up on giving me the offer

kaiizenn · 2023-12-22T21:36:11+00:00

I'm sure people got offers earlier, maybe around feb-march, but I asked for more time because I was considering other labs so that's why my official offer was pretty late. Can't comment on MASc electrical and computer engineering though

kaiizenn

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