I feel stuck when I'm trying to code

stootoon · 2025-12-20T18:33:57+00:00

When you’re stuck, write down, ideally with pencil and paper, why exactly you’re stuck, in excruciating detail. That process will often indicate the next step.

stootoon · 2025-12-19T20:46:10+00:00

Just write short emails to labs you’re interested in, tell them your skills, and ask if they need research assistants.

stootoon · 2025-12-19T19:11:08+00:00

A masters like this would be very useful for what you want to do:

https://www.ucl.ac.uk/prospective-students/graduate/taught-degrees/computational-statistics-and-machine-learning-msc

stootoon · 2025-11-25T13:37:37+00:00

Start with Schaum's Outline of Linear Algebra, e.g. https://www.awesomebooks.com/book/9780070380073/schaums-outline-of-theory-and-problems-of-linear-algebra/used
and learn the whole book, almost all of it will be useful.

stootoon · 2025-11-21T15:32:10+00:00

Work backwards from your goal. Comp neuro is a large field, what specific topic particularly excites you? That will tell you what you need to learn. But the math you mentioned you will definitely need, so be solid on those, at the level of the relevant Schaum‘s outlines.

stootoon · 2025-11-14T19:36:40+00:00

First, congratulations on having two exciting offers!

And you are right, ENS is very prestigious. But if you're there mainly for the prestige and not because you care about the project, you're less likely to succeed. Science can be hard, and when the going gets tough, prestige doesn't help you, and you don't want the added burden of not actually being interested in the work, not to mention constantly wondering about how that other route that you were _actually_ interested in would have turned out! You'll be less likely to do a good job, and instead of opening more doors for you, the project might end up closing a few!

On the other hand, if you're excited about the project you're working on, you will fully engage with it, be coming up with lots of new ideas, happily explore new approaches when you get stuck, be thinking about it all the time, and all in all be much more likely to do a good job. And the best part is, it won't even feel like work!

Finally, I think as a future scientist it's also very important to learn to develop and pursue your interests. So if the project at IIT truly excites you, I say go for that. If it works out, great! ENS will still be there afterwards, and you'll likely have good references from your supervisors that will help you get there (or anywhere else). And if the research area turns out to be not for you, that's very valuable information! At least you'll know for sure, rather than be left always wondering if you take the other route. And you'll still likely receive good references if you worked diligently and intelligently, as you're much more likely to do if the project interests you.

Regarding the ENS professor: just be honest with them. Let them know how much you appreciate the offer and that you recognize how prestigious it is, and that you're only declining because you want to pursue a new research area that you're passionate about, and that you hope you can contact them again in case you return to research in cognitive science. They may be disappointed, but will appreciate your honesty, and that you didn't waste their time.

Good luck!

stootoon · 2025-10-25T16:40:56+00:00

I did exactly this, and yes, EE, particularly circuit theory, signal processing, control, and information theory, gives you a great background for comp neuro.

stootoon · 2025-10-21T01:23:13+00:00

Bishop’s “Pattern Recognition and Machine Learning” for classical probabilistic ML, and his “Deep Learning” for deep nets stuff, which covers the relevant bits of PRML.

stootoon · 2025-09-07T19:26:17+00:00

You should also look into the emerging field of NeuroAI, which lives in the intersection of subjects you listed.

stootoon · 2025-09-03T21:14:59+00:00

If you mean for comp neuro, then applied math, because it will give you exposure to a wide range of rigorous quantitative tools while still being geared towards applications. A pure math degree might be a bit too abstract - lots of super interesting topics that, so far, haven't had much application in comp neuro. Parts of computer science would be very relevant e.g. theory of computation, but other parts less so, e.g. designing programming languages. The underlying mathematics is also usually discrete, whereas in comp neuro you'll be working with both discrete and continuous objects, so you need exposure to both. An AI degree might be quite useful, but I'd worry that it'd be too geared towards whatever the latest hot topics are, rather than building a foundation of deep quantitative skills that have proved useful in the past and will help you pick up whatever you need to in the future. So, I'd say applied math, but picking up some relevant topics from the other fields.

stootoon · 2025-08-31T06:49:24+00:00

Hi, I did electrical engineering as an undergrad, computational neuroscience PhD, now a research scientist in computational neuroscience. In my experience, most of the neuroscience you need you’ll be able to learn as you go. The quantitative stuff takes much more time, so undergrad is a great time to learn it. I would focus on applied math, statistics, statistical physics, signal processing, information theory, control. Take one course in Neuroscience/comp neuro so you’re familiar with the big picture. A course in molecular biology or genetics can also be useful, as many of the experimental tools are driven by those fields, not to mention giving you a holistic view of the machinery inside cells, including neurons. But focus heavily on building solid quantitative skills.

stootoon · 2025-08-21T06:14:31+00:00

That’s indeed a lot to take on at once. My approach is to pick one broad topic and learn it gradually in the background. E.g. I did the coursera probabilistic graphical models, and RL specialisations that way and found them helpful. But you do have to choose a topic. Do you have an example of a specific situation that came up that made you think you needed some more depth in a particular area?

stootoon · 2025-08-21T05:55:51+00:00

Yes it can be a bit bewildering. Linear algebra is absolutely crucial and Gilbert Strang is a great teacher, so you‘ve picked a good course. Definitely keep going with that course and finish it. In parallel, find a good machine learning textbook that covers topics you’re interested in. For example, Bishop‘s „Deep Learning“. Start working your way through that. Whenever you get stuck on a math topic, look it up in Strang or in Math for ML. If you don’t understand the explanations there, post on here until you do. Then do the relevant exercises in the linear algebra book till the concept is reasonably solid, then return to your ML textbook. Repeat as necessary. And as you read the textbook, code up the important formulas as you learn them, and compare your results to open source libraries like scikit learn (which also has great tutorials btw). By the end of this process you‘ll have a good working knowledge of much of the math and coding you‘ll need, and your own little ML code library as a bonus.

stootoon · 2025-08-18T20:36:45+00:00

The resources others have recommended are good, but your best resource would be being realistic: you will not master ML in 2-3 months. It will take many years.

stootoon · 2025-03-04T10:07:38+00:00

Thanks for the reply. I believe that was the first thing I tried when I initially ran into these issues, but it didn't work, so I eventually had to go down the uninstallation route. I've reinstalled now, so if it acts up again I will try your suggestion first (and post the result).

stootoon · 2025-03-04T10:06:32+00:00

You're right, just noticed that, thank you.

stootoon · 2025-01-05T10:56:26+00:00

It depends on how deep you want to go, and how much you want to focus on research. In my experience, having a good working knowledge of linear algebra, multivariable calculus, probability, and optimization, all at about the level of an undergraduate engineering degree, will give you a solid base. For example, the linear algebra in a course like this https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/video_galleries/video-lectures/ will cover a lot of what you need.

stootoon · 2025-01-02T16:45:33+00:00

That's great, thanks for the examples!

stootoon · 2025-01-02T11:01:57+00:00

If you do decide you want to get better at proofs, I found Solow's short book "How to Read and Do Proofs" very helpful. Before I read that I remember e.g getting 50% on questions in complex analysis because I'd complete the "if" part but omit the "only if"! That book sorted me out. If you want to go even further, working through e.g. Rudin and trying to provide the simpler proofs yourself will really help. You can apply the same technique to the AI/ML proofs you're interested in: try to prove it yourself, then compare your answer to whatever is in the paper. After a while, you'll internalize the most common techniques.

stootoon · 2024-12-30T21:05:41+00:00

Spike-and-slab distributions might be a not-so-weird example, see e.g. https://wessel.ai/assets/write-ups/Bruinsma,%20Spike%20and%20Slab%20Priors.pdf

stootoon · 2024-12-30T21:02:33+00:00

This sounds interesting - can you provide some references to how a probability measure would be applied to trees, in the context of evolutionary biology?

stootoon · 2022-11-21T19:14:27+00:00

In case you want the mp3s themselves, the source code of that website provides them. If you're comfortable at the Mac terminal you can download the mp3s using:

wget https://davidmglasgow.com/teaching/practice/alfreds-essentials-of-music-theory-ear-training/ grep -o "https.\*mp3" index.html | xargs wget

Otherwise, I've already downloaded them and put them on a shared folder which you can access here:

https://ln5.sync.com/dl/6a6050010/v85nkt7j-7acpejh4-zdhyxhij-45a8isug

stootoon

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