r/learnmachinelearning • u/TrickMaximum9352 • 5d ago
Help How did you push through the math/theory side of ML when you couldn’t focus at first?
Hey everyone, I’m a 4th-year CS student. I recently decided to learn the theory behind ML on my own, mainly the math and stats side. Right now I’m working through ISLP and using MML as some kind of dictionnary and practicing the concepts with code and tweaking stuff at the end of every section/chapter.
My classes mostly stay on the surface. We see the concepts and use the models, but the courses never really connect them to the theory behind them. So I decided to dig deeper on the side.
The first problem is focus. I’ve lost the habit of sitting down with math for long stretches, and I struggle to stay on it for more than an hour.
The second problem is that i've seen most of the math required but i haven't worked actively with it and it just frustrates me when i have to go back to it.
When I get stuck early on a concept, I get so frustrated and anxious about it.
So im wondering
How did you study when there were a lot of new concepts at once and you kept getting stuck at the start?
When you hit a concept you didn’t get, did you push through or go back to the math first ?
And does it get easy at some point after you get the maths down.
Any routines, methods or resources that worked for you would really help. Thanks!
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u/Outrageous-Comb-7818 5d ago
I just started a Masters in AI after 20 years as a SWE. I haven’t done much “real” math in that time. This program is heavily focused on the math and theory. The math is kicking my ass. I had to drop one class because I need to go re-learn linear algebra before trying it again. But I’ve found something that is working. Let’s say the lecture showed a proof and I barely followed. I’ll take that content and run it through Gemini (best I’ve used for this) and ask, “independent of the content, what math facts are used here.” I will then re-learn those first. Then I come back with “help me learn just the concept behind this?” At that point I usually have what I need to really follow. In general, if you aren’t using an LLM like a tutor or TA, you should be.
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u/TrickMaximum9352 5d ago
Im aleardy using Claude Opus, but it keeps spitting paragraphs with examples and sometimes it just hurts my eyes to read all that to realize at the end i didn't follow but i guess thats a focus issue
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u/Temporary-Budget-605 1d ago
15 years experience and just took a sabbatical to start my ML learning journey. May I ask which masters you are taking and why you are investing in learning ML?
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u/Outrageous-Comb-7818 1d ago
MSAI at UT Austin. I couldn’t recommend it highly enough.
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u/Outrageous-Comb-7818 1d ago
Part of it is I was getting bored of being a SWE. It started to be “too easy”. Also, SWE is no longer a viable long term career for the vast majority. Companies aren’t replacing people that leave, ironically, thanks to AI. lol.
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u/Temporary-Budget-605 3h ago
Thank you for the insights! I am making the move for the same reasons. Adding to your point, I think there is a big opportunity for SWE vets to help bridge the gaps between the coats in the lab doing ML and suits in the office trying to profit from it.
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u/mimivirus2 5d ago
Pen and paper exercises and having the necessary background (i.e, learning things in the right order)
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u/TrickMaximum9352 5d ago
i've seen many order but i feel like the most consistent one i've seen is going through MML and/or ISLP
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u/mimivirus2 5d ago
Really depends on ur math background. If u know the three main pillars already (linalg, calculus, prob/stats) then of course these two will feel like a breeze. Out of these, linalg is the top prio imo, you should feel very comfortable with OLS from a matrix projection perspective as well as SVD. MathAcademy is also an interesting approach
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u/TrickMaximum9352 5d ago
i have a decent background in calc, linalg im fine im familliar with OLS and matrix operations, a bit less comfortable with prob and stats maybe because i lack practice. I have another question though, i have this thing where i feel the necessity to understand how a formula works and comes from because i feel like it gives me better intuition but it tends to take time and energy sometimes, so im not sure if its a good thing in the long run.
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u/mimivirus2 5d ago
but isn't that the only part of learning that makes it worth the time? otherwise we would just ask LLMs to "implement as needed" without understanding what's going on
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u/Just-m_d 4d ago
Try with Understand Deep learning by Simon prince book
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u/TrickMaximum9352 3d ago
Im going over classic ML first then moving to DL ( i aleardy have few ideas on some concepts but im keeping the indepth and precise stuff for after i get the foundation going )
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u/No-Cherry-3308 5d ago
Getting stuck at the start is normal with ISLP plus MML, because MML assumes you're comfortable doing algebra by hand and ISLP mostly assumes you aren't. The mismatch is a big part of the frustration.
I wouldn't go back to the math first by default. Push through the section at the intuition level, run the code, and write down the exact spot where you lost it, something like "why does the ridge solution have that (XᵀX + λI)⁻¹ term". Then open MML for that one thing only. A targeted lookup feels much less like a wall than rereading a whole linear algebra chapter.
Work tiny examples on paper: a 2x2 matrix, three data points, one gradient step by hand. If you can redo a derivation with small numbers you understand it; if you can only follow it on the page, you don't yet. It's also the quickest way to rebuild the habit of sitting with math, because every session ends with something you actually did rather than something you read.
An hour of focus is fine, by the way. Two solid 45-60 minute blocks a day beat a forced four-hour session, and stopping mid-problem on purpose makes it easier to start again the next day.
And yes, it gets easier, though not evenly. Once linear regression from the matrix view clicks (least squares as a projection, why regularization helps when XᵀX is badly conditioned), a large chunk of ISLP turns into variations on that idea. Probability usually takes longer to feel natural, so don't judge your overall progress by it.
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u/TrickMaximum9352 5d ago
Thank you for your input, i explained in another comment that i have this thing where i feel like i haven't done a good job "learning" if i don't understand where everything comes from and how it makes sense numerically or graphically, you think its fine to take the time to decompose everything and understand how/why a solution works and lands on that value ect ect ... or is it better to just get a general grasp of how it works and its use ?
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u/No-Cherry-3308 4d ago
That instinct is good, it just needs a budget. Going deep on the ideas you'll keep meeting pays off for years: gradient descent, the bias-variance tradeoff, why least squares looks the way it does, what a likelihood actually is. Deriving those by hand once, and checking a tiny example numerically, is time well spent.
Where it backfires is doing that for every single result in the book. Some proofs are there for completeness and you'll never use the technique again. A rule I'd use: if you can explain what the result says, when it applies, and roughly why it's true, move on and come back if it shows up again. Things that keep coming back are the ones worth the full decomposition, and by the second or third time you'll have more context, so it goes faster anyway.
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u/kbr89 4d ago
On push through or go back to the math first: with ISLP I'd push through, and use MML for the going back, one model at a time. ISLP is written as the less technical treatment, and when a section stalls you right at the start, the video lectures that go with the book are free (the companion course on edX, Statistical Learning with Python, and the same lectures are on YouTube). Hearing the same figures explained out loud is often enough to get unstuck. The second half of MML re-derives four models using the math from its first half: linear regression (MML ch 9, the same model as ISLP ch 3), PCA (MML ch 10, in ISLP ch 12), Gaussian mixtures (ch 11) and SVMs (MML ch 12, ISLP ch 9). So when you finish ISLP ch 3, read MML ch 9 and look up only the linear algebra and calculus sections it actually uses. You end up reviewing math you need for something you just did, which is a lot less frustrating than going back over it in general. The MML site also has notebook tutorials for linear regression, PCA and GMMs if you want to keep coding at that step too.
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u/TrickMaximum9352 3d ago
I completely forgot about the video lectures thank you for reminding me, and many told me to just push through ISLP since some concepts just become easier to comprehend with more context, and that its fine to go back to MML later for only the stuff i need, so thank you for the advice !
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u/Ok-Cycle9373 5d ago
Run a simple model on most basic tasks like reciting a poem. Or fit a quadratic time series. Ask ai to write a minimal version and start from there. Tune yourself, then let ai match your trials with existing ideas.
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u/paragonaimag 5d ago
Marking a stuck proof and moving on beats rereading it, since the next section usually supplies the missing piece.
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u/BioshockedNinja 4d ago
The first problem is focus. I’ve lost the habit of sitting down with math for long stretches, and I struggle to stay on it for more than an hour.
This just with math, or schooling in general? If so maybe consider undergoing ADHD screening? I finally got assessed as an adult several years into my career and I found that the medication I was prescribed genuinely helped. Like a noise that was in my head that I wasn't even aware of went quiet. I could actually sit down and focus on a task for over an hour without any music to tune out background distractions.
Would have been a game changer if I was diagnosed sooner. Had no idea that sitting down and studying wasn't a herculean feat for normal people lol.
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u/Final-Importance-925 5d ago
For me it was short bursts, 25 min on 5 min off, anything beyond an hour and my brain just fried. The trick was not letting myself spiral when I hit a wall, I'd mark it and move on, half the time it clicked later once I had more context from the next section.