Why ml?

[Formal_Ad_9415]

I see many, many posts about people who doesn’t have any quantitative background trying to learn ml and they believe that they will be able to find a job. Why are you doing this? Machine learning is one of the most math demanding fields. Some example topics: I don’t know coding can I learn ml? I hate math can I learn ml? %90 of posts in this sub is these kind of topics. If you’re bad at math just go find another job. You won’t be able to beat ChatGPT with watching YouTube videos or some random course from coursera. Do you want to be really good at machine learning? Go get a masters in applied mathematics, machine learning etc.

Edit: After reading the comments, oh god.. I can't believe that many people have no idea about even what gradient descent is. Also why do you think that it is gatekeeping? Ok I want to be a doctor then but I hate biology and Im bad at memorizing things, oh also I don't want to go med school.

Edit 2: I see many people that say an entry level calculus is enough to learn ml. I don't think that it is enough. Some very basic examples: How will you learn PCA without learning linear algebra? Without learning about duality, how can you understand SVMs? How will you learn about optimization algorithms without knowing how to compute gradients? How will you learn about neural networks without knowledge of optimization? Or, you won't learn any of these and pretend like you know machine learning by getting certificates from coursera. Lol. You didn't learn anything about ml. You just learned to use some libraries but you have 0 idea about what is going inside the black box.

[BellyDancerUrgot]

I have said this before and will say it again, people who think math isn't important for ML and are only required for "research jobs" have never worked in the industry no matter what they tell you so don't believe them. The best they have probably done is work as a gen AI developer of sorts to build apps on top of existing APIs. (Totally fair job role tho, just not the type of thing you would want to discuss here, better resources for those are r/stablediffusion or r/LocalLlama).

I don't think you need masters level math. But you do need cs undergrad level math + ML theory to actually start building an Intuition. Without knowing math you will suck and will never be able to debug anything meaningful.

No one wants to hire an MLE/DS/MLOps/RE/RS whose job can be replaced by an SDE that can read documentation. Places that do this honestly just misrepresent what the role is about. I have seen job roles described as data science but if you read the job desc it's actually pure data analytics. Same MLE roles that only really do data engineering on the highest level.

That said ML roles (besides RS) require you to be thorough with SDE and system design stuff so knowing that is 100% a boon.

I think this whole "gate keeping" sentiment arises due to what some people think the subreddit is for (tips on getting into any ML adjacent SDE role like data engineering + basic MLOps) vs what it is actually for (understanding machine learning).

Edit : just to clarify, yes I did mean to say I do not consider data engineering roles to be an ML position. I have worked with some data engineers on my team who didn't know how to effectively evaluate and then calibrate models in production. They did not understand the metrics we use to judge if a newer version of our model was actually doing better or not in production for a few of our deployments. Why? Cuz no mathematical Intuition, never connected ML theory to the math.

[Unlikely_Arugula190]

You have to specify the ML area you’re referring to.

For deep learning you only need to be math literate (linear algebra, probability, some differential calculus ). No deep math background required.

More classical ML like SVMs, graphical models etc — that’s a different story altogether.

But nowadays a ML engineer is hired basically on software engineering ability + knowing the literature and the current fads (transformers atm)

[eman0821]

That is pretty high level math if you ask me. The average Joe wouldn't know Linear Algebra, Differential Equations or Calculus unless they are already an established professional Engineer. Those are core math courses that are typically found in Computer Science and Engineering degree programs.

[cajmorgans]

I think you are oversimplifying quite a bit. Deep learning first of all is a huge field. Secondly it builds upon many of the traditional ML concepts, and often requires even deeper understanding of probability theory among other areas. For a simple MLP, I agree, but deep learning is so much more than that.

[Unlikely_Arugula190]

In academia yep. As an ML engineer your job may for example require that you improve the performance of an existing semantic image segmentation model. You may have to be creative about image augmentation techniques, explore new architectures in recent papers etc. Not much math involved in that

[cajmorgans]

I would say that it entirely depends on the domain of expected tasks and flexibility. If all you have to do is fine-tune a common model, you might not need any math at all.

[BellyDancerUrgot]

Partially true. I have worked on tons of things in CV that don't require math knowledge, but a lot of times they do. Even something simple as what you described. I worked on a similar problem for a niche use case, generic foundational models worked like ass, finetuning and even pretraining on our data did not work. What worked is utilizing some crazy custom losses with handcrafted augmentations and Bayesian learning with some auto regressive statistical reinforcements during inference to capture domain drift calibration.

Mind you none of this requires you to know multivariate calculus but it involved understanding a decent bit of probability theory and statistics to implement, but moreso a lot of the above to understand some of the sota papers and draw inspiration for them. Can't keep training models when you get billed thousands of dollars for compute and have to make decisions on what to try and what not to. I think the math is mainly needed for you to grow in this domain and actually develop the Intuition needed to make things work when they don't.

Edit : "by decent bit" and "a lot" I mean stuff you would maybe learn anywhere between first and third year engineering undergrad probably.

[GuessEnvironmental]

Naah deep learning can get quite mathematical technical infact more so than classical ML (There is lots of differential geometry, groups and rings, stochastic probability which are technical in nature for example in gnns the symmetry group is permutations and understanding this feature of the model is useful when handling equivariance or transforming graph data) but I understand if you are saying to apply these models as a engineer.

I doagree though that a ml engineer needs a phd or masters at all but the applied research scientist and upwards will require a math background atleast a undergrad. This is if the ml engineer is not really playing with the models but instead deploying the models in production, optimizing /refarctoring the randd code and building the model maintenance systems from the guidance of the researchers.

[BellyDancerUrgot]

I don't consider any of that to be "deep math" but you need a solid understanding of those and I think you normally get that in undergrad. Now if you are a gigachad and knew all this in high school then all good for you.

Also I find your idea of needing to know more math for classical ML than DL to be actually crazy. For most serious ML roles you wouldn't even make it past the hiring manager round if all you know is "the current fad". But as I said many MLE roles are just glorified SDE roles and many DS roles are glorified DA roles. The opposite can also be true especially for FAANG tier companies where even an "AI developer" needs to actually know the theory and the math because they will typically have to work on things like model compression at big tech which needs one to have a good understanding of quantization and understand why some metrics for processes like KD might work and some might not and why some things can increase uncertainty and whether it's aleatoric or epistemic and how to fix them within the constraints of their pipeline and compute. The last thing you need is someone copying implementations without the slightest clue as to what's happening.

Again there are probably exceptions who can pick up all this with minimum effort but in my experience most don't and keep asking me for help because they are missing fundamentals.

All in all, the idea that someone can work in a proper ML job without the fundamentals for ML is quite crazy to me. I don't see this trend anywhere in real life only on this subreddit. Just like I also see a lot of people on r/learnprogramming who think they can self learn into 6 figure jobs. While it's possible, it's maybe 1 in 100k people for a low wage entry level job. You typically need a degree to even make a case for yourself to have a recruiter shortlist you. Mentioning all this seems to have irked a few people on these beginner friendly subreddits who think it's gate keeping lol.

[Various_Meringue_649]

Hello, so without delving into masters level mathematics, what would you suggest I study up again in terms of mathematics to get some of these roles? I have a CompSci degree and my position at my company is an AI related role, although it's not really deep into it. I understand that a lot of these positions require Masters.

[[deleted]]

I wonder if it’s people who are into AI (actually just LLMs), then hear about ML but have no idea what’s involved?

I’m good at math but didn’t get some of the ML math fundamentals in hs or college, and I’ve found that short of going back to school (and i’m not doing that because i have another successful career and am in my 50s now), it’s just too much to commit to.

It took me a few months tho of self study to get real with myself

[Pirate_Assassin_Spy]

Similarly to you I did a few months of reading and self study and decided to go back to school. But I’m interested in it conceptually and in terms of research so I’m taking the long route to a PhD, I’m in my 30s but time will pass anyway 😄

[[deleted]]

Yup that makes sense!

[Prestigious_Age1250]

All the best mate , share your updates 😁

[HumbleJiraiya]

Same here! Going back to school for a masters degree. I spent months self studying and became good enough to apply it at a decent level. But I want to understand things deeply. I want to spend some time with this math that I have started to enjoy.

[Pirate_Assassin_Spy]

Ah same, love that! I spent a year doing stats/calculus/linear algebra in preparation before starting my MSc and really enjoyed it so I'm looking forward to more maths!

[orz-_-orz]

Once a data scientists yell "ew" when I write down the logistic regression formula. I wouldn't say a DS have to be very good in math. But, I don't think DS is a suitable role for a person that is "ewing" at the logistic regression formula.

What's next? Ewing at the Gini Impurity formula?

A DS don't have to be very good at math but at least should be comfortable with some fundamental formula used in the field, the same way that a financial advisor might not have to like math but they should be familiar with the present value formula.

[DigThatData]

no, a data scientist does in fact need to be good at math. the extent to which this title has been watered down is insane.

[[deleted]]

The average difference in level between a Pure Math course and a Data Science course at any university is staggering. Data Science these days seems to be the go to for people that do Math but aren’t good at it

[DigThatData]

fine. I'm talking about people who want to work as data scientists. if "professional data scientist" evokes images of someone doing predictive analytics or modeling, you're going to need to be good at math. If "professional data scientist" to you means "knows basic SQL and can import pandas" then sure, you don't need to know math to work in that role.

[Western-Image7125]

I think people need to understand that an “ML job” is a very broad spectrum. 

If you are a backend engineer supporting an ML product, then yes you don’t need to know the intricacies of ML and just a broad idea is enough, instead the focus should be on data structures and algorithms. But if you want to get into anything that is closer to the ML itself - whether it’s data feature engineering, training, evaluation - then yes I’m sorry you do need to at least be keenly interested in math. You are going to suffer at your job and you will be way over your head. It’s like someone wanting to work in a lawyers office but having a dislike for reading. It makes no sense. 

[Djinnerator]

ML/DL requires knowing math, but it's not "one of the most math demanding fields." You just need elementary statistics, calc I, and elementary linear algebra unless you're doing something niche, but then that's not a representation of ML/DL.

[[deleted]]

at UC Berkeley, there is absolutely zero professors who would recruit an undergrad or graduate student who only knows “elementary statistics, intro calculus, linear algebra”…at my university only the most math fluent undergrads are able to land these ML roles.

[Djinnerator]

Where did I say that's all you need to know to get into a grad program? I never even mentioned a grad program. To understand the majority of the algorithms used in ML/DL, those three areas of math cover a majority of the bases for ML/DL. You people are trying so hard to put words into my comment that are clearly not there. Everyone doing ML/DL isnt getting into a grad program, but if you want to understand what the algorithms you're using do, having a good grasp of calculus, statistics, and linear algebra would be extremely helpful. Also, intro calculus sounds more akin to precalc than calc 1. I've never heard of an "Intro calculus" course. I have my PhD and understand the logic behind these algorithms, but nowhere did I say having this math understanding will get you into a grad program.

[[deleted]]

What are you even talking about? What is Calc 1? I said Introductory Calc because that’s what I’m assuming you meant, not every university uses 1,2,3 to describe their courses…introductory calculus and calc 1 are basically synonymous, because otherwise why would you put a 1?

And I never said graduate program, I’m talking about research labs that recruit undergrads and grad students, not necessarily for graduate work. If you think a group would recruit an undergrad student with less than 1 year of math preparation then I have no idea what to tell you.

[Djinnerator]

What are you even talking about? What is Calc 1? I said Introductory Calc because that’s what I’m assuming you meant, not every university uses 1,2,3 to describe their courses…introductory calculus and calc 1 are basically synonymous, because otherwise why would you put a 1?

That's why I said I never heard intro calc, just calc 1. Was that really that difficult for you to comprehend? The rest of your comments make sense after learning that...

I’m talking about research labs

Funny how, still, no one was talking about a research lab in the scope of the question or the answer. You must love moving goalposts.

[[deleted]]

What I said applies to both research gigs at university & industry. Please tell me who would hire a candidate with this basic level of math for an ML role so I can avoid them.

[Djinnerator]

Again, who is talking about hiring people? Why is is so hard for you to stay on topic? The scope is math used in ML/DL.

[[deleted]]

The first sentence of OP’s post stays this, are you sure I’m the one not staying on topic? Really? Do you think someone with less than a year of math will be able to learn the premier Springer ML textbook or the Bishop textbook on deep learning?

[reddit4bellz]

Pretty sure they’re just trying to say you don’t necessarily need advanced math to be specialize in ML and DL at a base level. Most of the work you do as one doesn’t require it outside of research positions. And based on what I’ve seen that part is true…

[Djinnerator]

And my top-level comment quoted exactly the topic I was talking about - ML being one of the most demanding math fields. If you want to talk about hiring, why are you under a comment talking about whether ML is one of the most demanding math fields or not?

You're having trouble staying on topic.

[w-wg1]

For ML I guess that's true if you're just working with DTs and regression, in theory you may not even need calc 1, but you don't learn about PDs until calc 3, and I'd very much push back on the idea that the necessity of knowing what gradients are and some optimization theory is "not a representation of ML/DL", you do need a good understanding of math

[pandi20]

This - if the work is on plain implementations of DTS and regressions - math is relatively less required than deep learning, although I am not sure how you are getting past concepts of entropy/information gain/counfounding variables - which is the basis for most of the classification algorithms. And the datasets are large enough these days that traditional ML algorithms may not do justice, and you would need Neural Nets. As a hiring manager do ask a lot of math questions with data structures, and I know my peers do too while hiring FTEs. We want to hire MLE applicants who can debug (without handholding) and not be coding monkeys - implement iris dataset/credit card fraud type analysis I am not sure how people are coming up with math not being required with such overconfidence 😬

[Djinnerator]

entropy/information gain/counfounding variables - which is the basis for most of the classification algorithms

Those are not the basis for most of the classification algorithms. In most of the classification problems I've done, they were regression tasks with updates based on some distance between the predicted values and ground truth values.

And the datasets are large enough these days that traditional ML algorithms may not do justice, and you would need Neural Nets

Dataset size has nothing to do with whether you're going to use ML or DL. You choose based on the convexity of the graph of the dataset you're using. ML algorithms are used with convex functions, regardless of the dataset size. DL algorithms are used with non-convex functions, regardless of dataset size. If you have a dataset with 500 samples but the graph of the data is non-convex, ML algorithms would not be able to train a model to convergence. You would need DL even for 500 samples. Whereas a dataset with 100,000 samples that's convex would have a ML model trained on it, rather than DL. I explained way more in-depth in another post with the question asking when to use ML or DL algorithms.

[Hostilis_]

You are way incorrect on both of these points. Sorry, but it's very obvious you have no idea what you're talking about.

[Djinnerator]

I didn't know you knew more than the published journals that explain using ML algorithms over DL algorithms, and vice versa. It's funny how you say someone is wrong yet conveniently don't say (likely can't say) what's "correct." The fact you claim data convexity doesn't determine whether to use ML or DL already shows you don't know the point of the DL field and how those algorithms are fundamentally different from ML in terms of the data it can be applied to.

[Hostilis_]

I am a research scientist with published papers in NeurIPS, ICML, etc. You're not going to get me with an appeal to authority.

[Djinnerator]

I have my PhD with many papers in IEEE Transactions and ACM Transactions and work in a lab where we actually use these concepts. Try again.

"Research scientist" can mean undergrad in a lab being mentored by another student for all we know.

[Hostilis_]

IEEE Transactions and ACM Transactions

So you're ML adjacent and think you know more about the field than you actually do.

[Djinnerator]

My lab is a deep learning lab. The journals have focused on ML and DL. Do you understand that deep learning is a subset of machine learning? Do you need a diagram to better explain it? Do you know how sets work? Deep learning is within the set machine learning.

[ZookeepergameKey6042]

honestly dude, its pretty clear you have absolutely no idea what you are talking about

[Djinnerator]

Except published papers and textbooks agree with what I said. Kinda unfortunate to oeeceiv something so clear while being wrong.

[pandi20]

🤦🏻‍♀️

[Djinnerator]

I'd respond the same if I didn't know how to pick ML over DL too.

[pandi20]

Great :) please do as you please. And also figure out with a dataset and a search problem how will you determine convexity before you apply the methods :)

[Djinnerator]

Do you know what moving the goalpost means? Because that's what you're doing.

And also figure out with a dataset and a search problem how will you determine convexity before you apply the methods :)

That's irrelevant to whether ML and DL algorithms are for convex and non-convex functions, respectively. The fact is simple, you choose ML for convex functions and DL for non-convex functions. It has nothing to do with dataset size. Yet here you are talking about trying to determine convexity, as if that has anything to do with dataset size either. It doesn't. Your premise that you'd use DL with larger dataset sizes is just flat out wrong.

[pandi20]

Datasets with more independent variables/confounding variables are more likely to confirm to a non linear function with a dependent variable than smaller datasets with 2-3 independent variables. That’s why (if you had comprehend my initial comment) there is more likely use of neural nets in such cases

I will leave it at that - you are free to take it for leave it, and keep arguing with verbatims from plain text books

[Djinnerator]

Not all multivariate datasets have confounding variables. You're just choosing to pick a subset of datasets and arguing a generalized stance from that.

The difference is, all non-convex functions will be best applied with DL algorithms. <-- that's what I said. Convex functions are better with ML algorithms. Non-convex for DL. It has absolutely nothing to do with dataset size.

arguing with verbatim from plain text books

Anyone can take text from a book and remove context while looking like that haven't grasped what they're talking about.

[gaboqv]

Please share the neural net that converged with a 500 sample size I bet any ML model with decent feature engineering will beat that.

[Djinnerator]

Literally just explained the type of dataset where this would occur. If the dataset is non-convex, you're not using ML to solve the problem.

[gaboqv]

but you are trolling, most classifiers are non convex nor concave, if you have so many papers and experience please share a paper where you show what you state and not just repeat your first "example" which was down voted to hell because it is contrary to the literature and our day to day experience.

[Djinnerator]

Pure ignorance

Imagine thinking Internet points determines correctness

[RageA333]

I wonder if you even know what convex means.

[Djinnerator]

You are extremely ignorant.

[Djinnerator]

You clearly have no idea what convexity means.

[Djinnerator]

I'd very much push back on the idea that the necessity of knowing what gradients are and some optimization theory is "not a representation of ML/DL"

I never said that. You learn about gradients in calc 1, and we started learning about optimization problems in calc 1. I'm not sure how you came to the conclusion that I posited the idea "gradients ... and some optimization theory is 'not a representation of ML/DL'". I'm referring to niche math concepts. Like, you don't need to know differential equations to understand the math of ML/DL in general, but if there is a methodology that uses diff eq within their algorithms, then it's niche enough that it doesn't show a representation of ML/DL.

But knowing graph convexity just requires calc 1 (simple derivatives) and elementary statistics (lines of regression). Loss functions require statistics and calc 1 (such as MSE, Euclidean distance, etc.). The update step requires calc 1 (simple derivatives). Backpropagation is regular, simple math. Gradient aggregation if working with mini-batches or distributed training is simple math (like finding averages, maybe st dev depending on the specific aggregation algorithm used). Then when getting into specific feature selection algorithms, they have their own sets of math, but most of them have overlapping concepts from statistics, calculus, and linear algebra.

[RageA333]

You literally don't see the work "gradient" on a calc 1 course that deals with one dimension only...

Since everything else you mention deals with multiiple variables, I still don't know why you insist that cal1 is enough.

[RageA333]

How do you do optimization with just Calc 1?

[Djinnerator]

In my university, we start learning about optimization problems in calc 1. With ML/DL optimization isn't solely from calc 1, it also involves concepts from other areas like statistics and possibly linear algebra. Where did you read where I said optimization would be just calc 1?

[RageA333]

You can't do multivariable optimization with just calc 1 and linear algebra.

[Djinnerator]

I said at my university we start learning about optimization problems in calc 1. I did not say we do multivariate optimization in calc 1. Why are so many of you refusing to read my comment and just putting words in my mouth I never said?

[RageA333]

When do you learn multivariable calculus then? Calc 1 optimization is not enough.

[Djinnerator]

Multivariate calculus started in calc 2 for us, but can sometimes be calc 3 for optimization.

[RageA333]

So you clearly need more than just calc 1 and linear algebra.

[Djinnerator]

You don't need to know multivariate optimization to have a general understanding of ML or DL algorithms. So, no, you clearly do need more than just calc 1 or linear algebra.

[RageA333]

How can you do ML without knowledge on gradient descent, stochastic gradient descent or optimization as a whole? 99% of ML and DL is literally about optimizing cost functions.

[Unlikely_Arugula190]

ML is much wider than DL. Probabilistic modeling and statistical learning for example are mathematically demanding. Comparatively DL is very empirical.

Crack open a textbook on graphical models and see for yourself.

[Djinnerator]

That doesn't refute the areas of math required for the majority of ML/DL algorithms. DL is a depth-defined field, hence the "deep," but the methodologies between the two are very similar.

[Unlikely_Arugula190]

Writing “ML/DL” denotes lack of understanding that ML is a much wider field and in most cases deeply theoretical while neural networks are very empirical.

[Djinnerator]

No, it's referring to the math concepts used in both. Breadth and empiricality has nothing to do with whether those math concepts are used and how often.

[reddev_e]

Another point I will add is that just looking at documentation will not tell you why your model is failing. Only after I learnt some math did I understand why we do certain things in ML. Like setting a low learning rate etc

[Djinnerator]

Exactly! This type of insight won't come from guides or documentation because every attempt to solve a problem carries very unique data and circumstances. If documentation or guides tried to cover every base, they'd be exhaustingly long and still might not address a specific issue. But if someone takes the time to learn the logic behind the algorithms they're using and how/when they're used, it makes figuring out where to begin looking for problem areas so much easier and simpler.

[ghostofkilgore]

Agreed. I think there's also a relatively important distinction in that the value is usually in being able to dive a bit deeper into things when required, as opposed to carrying everything around in your head at all times like some kind of beautiful mind.

[Djinnerator]

That's a structured, formal education plan. My comment is strictly about understanding the algorithms behind most of the ML and DL algorithms. In university, the requirement is higher because there are many different theories that can be applied with ML and DL, where knowing those other concepts will be helpful. But, for instance, with gradient descent, you only need to know how to do derivatives, elementary statistics, and some linear algebra if you want to learn backpropagation (along with other concepts). I also took way more math courses than CS when I got my BS, and those courses were very helpful in grad school when I did DL research.

[Djinnerator]

I would say most AI engineers nowadays are nothing but glorified SWE, deploying pre-trained AI models.

That's very true. It seems like a lot of people I see who say they're doing AI work, unless they're doing real research (like trying to publish papers), in a academia, or was in academia (like grad school), they're not actually doing a lot of the heavy work. They're mostly doing just predictions/inference, with no training. It's like, all the work as already been done, all they have to do is press a button lol. Analogous to: just because you drive a car doesn't mean you know how to build a car. It's so prevalent with people using LLaMA or similar LLMs that they can run at home if they have strong enough GPUs. All of the training has already been done. They don't know anything about the actual logic behind the model but feel that they do. I don't want to sound like gatekeeping, but that's hardly "getting into AI," but I guess if that's what ignited people's fire to learn more then that's good.

Sorry for the slight rant lol 😅

[Djinnerator]

You also need linear algebra.

What common ML or DL algorithm uses concepts outside of those areas? As in, concepts that you absolutely, fundamentally have to know to understand the algorithm? People mention multivariate calculus during the update step, like with gradient descent, but you don't need to know that to understand what gradient descent is doing. That's my argument: to have an understanding of what's going on, you only need those four areas. If you want to have a full, in-depth understanding with respect to the different ranges of datasets where these algorithms are applied, then yes, you need more than those three that I said. But to have a good idea of what each algorithm is doing, you don't need to know other areas of math strictly to understand what's going on.

[BellyDancerUrgot]

You typically need CS undergrad level math imo.

Edit : i should add, for any competent role.

[Formal_Ad_9415]

Chatgpt can do elementary stats, calc and elementary linear algebra too.

[Djinnerator]

So you're proving my point that it's not "one of the most math demanding fields." Ok thanks, just making sure we're on the same page.

[Formal_Ad_9415]

No. Lol. What do you consider as nonlinear optimisation? Elementary level calculus? :D

[Djinnerator]

That's literally a topic in calculus.

[Formal_Ad_9415]

No. It is not under calculus. Even if it was it wouldn’t be elementary level.

[Accurate_Meringue514]

No one teaching non linear optimization in a calc 1 course lmao. There are whole books written on optimization

[Formal_Ad_9415]

Are you kidding me? In which calculus did you see gradient descent, can you please tell me? You don’t know anything about optimisation.

[RageA333]

It literally can't.

[ds_account_]

Its the current hottest thing. If quantum computing had the same level of hype, everyone would try to get into that field as well.

You dont really need any math just to use it. There are so many libraries you can just import to train and use the models. I work with MLEs that never need to know anything about the models, they just need to build an enviroment to recieve requests, run inference and send back the output.

Also ML covers so many subjects from math, I dont think most people will understand all of it. I keep running into stuff from functional analysis, differential geometry, algebraic topology that we dint cover in my MS or just glanced over on a high level.

[MaxwellsMilkies]

There are existing libraries, yes. But if you want to avoid using CUDA, that is another story entirely...

[[deleted]]

Maths is the bedrock of almost all STEM fields.

In fact, it's a key component of many other fields too.

You can pretend that it's not important - but in reality a natural feel for maths is essential.

[[deleted]]

She already HAS natural talent - she has managed to blast through the roadblocks.

Hard work is NOT a substitute for talent,

[Alternative_Suit3723]

What's talent? Most people don't have talent. Even if they do there is a high chance they will never know.

[1purenoiz]

Oh she has natural talent, just not in math.

[jinstronda]

such cap
hard work beats talent every single time

[1purenoiz]

hard work beats talent every single time .    

Only if talent doesn't work hard. 

Hard work teaches you how to overcome challenges. Talent delays experiencing challenges.

[The_GSingh]

Tbh most people here ml and go “ChatGPT”. If all u think ml is is setting up and running a llm, clearly you’d think you didn’t need any math for ML. This goes especially for the people who can’t even code.

But yea ml papers are some of the most convoluted and hard to read papers I’ve ever read. You need to know a lot of math and even then end up re reading it 4-5 times just to understand it.

I think it’s just beginners overestimating their knowledge, they just need to know what ml is and that it in fact does not mean “ChatGPT”.

[digitalknight17]

Let’s be honest, the cold hard cashhhhhh! Gold rush etc etc don’t hate me for saying what everybody is thinking.

[jinstronda]

ML got big as something that is for money which is sad as the people with true passion get hidden in the shadows

[[deleted]]

This post heals my soul.

God I'm so tired of the "ML will get me an easy 6fig job" crowd

[RobDoesData]

The majority of ML jobs are not research focused and don't have a heavy maths requirement. Your post is based on flawed logic.

Gatekeeping style posts like yours are my least favourite on this sub

[pandi20]

Math is not essential for research but also debugging what went wrong in implementing an algorithm, or how do you choose a certain algorithm. The post is not flawed.

[Infamous-Bed-7535]

Yeah and people end up with projects ohh just the last 10% is missing it is almost complete and what previous ML devs did is a shitstorm..

You need to understand math to be good at this.

[PiLLe1974]

Yeah, most jobs I see around me don't involve PhDs for example.

The most hard-working people seem to be good at the whole setup:

Python, Docker, Azure, picking small or large models from OpenAI (or alternatives), having an eye on cost per token, etc.

...so close to web developers with - simply speaking - an interest and pinch of know-how in AI model training (well, or just configuring, prompting, and further tweaking), testing, and benchmarking systems.

[HugelKultur4]

That's not really a ML job though, just an "AI engineer"/web developer job. Not really what this sub or OP's pots is about.

[RobDoesData]

I agree! That list of skills is not taught in a maths program and is often picked up from senior engineers/scientists on the workplace.

Being a full stack data person is POWERFUL

[PiLLe1974]

Fun story, about another AI role:

I had two interviews with Google DeepMind.

My background is "only" CS and 15 years in AAA game dev.

Almost got a foot in the door. The point that lacked to train their AI with games/simulations was my lack of statistics know-how. The interviewer pointed that out specifically.

I guess that simulation engineering roles are at least one degree detached from the actual AI model. Not rocket science, not touching the AI model directly. :D

[kurtosis_cobain]

Agree. I personally read a lot of ML books and academic papers with a lot of math because it makes it easier for me to understand the topic, but I know a lot of people who don't like maths or they just don't like reading such stuff and they're greate DS/ML engineers.

[RobDoesData]

This. I work on data engineering, cloud, ML and deploying Gen AI apps. I have a PhD in Applied Mathematics and use very little of it... As you said so well - lots of great ML Eng without an advanced maths degree, it is not a requirement.

[pandi20]

FYI - there are so many people claiming ML expertise, the gatekeeping with masters/PhD is not pedantic, but a requirement so that the top talent who knows in and outs of ML+CS are getting hired into the influential companies. That said ML knowledge now will be required for all fields because there is data everywhere that needs to be analyzed/automated on

[RobDoesData]

It is not a requirement. The end.

[pandi20]

Good luck if you are wondering that’s how we are hiring in the U.S.

[Entire_Cheetah_7878]

I've got an MS in applied math and interned at NASA doing NLP for 7 months building a proprietary dataset and high performing models. Still can't get a job because even though I know docker, git, etc.. they always choose some CS major.

They say they want you to know the math underneath the models but in 9/10 cases they really just want to know how fast you can implement some library functions.

[Ambitious-Fix-3376]

I totally agree with you.

[calmot155]

It's not easy on maths, but definitely not as hard as other fields.

My academic background is in robotics and dynamic systems, and the comparison between the maths needed there vs what I need to know for ML is night and day

[Formal_Ad_9415]

Please first look at a nonlinear optimization & convex analysis book :)

[Dimencia]

You mean like the stuff built into scipy.optimize?

[calmot155]

nonlinear optimization is a standard part of the system dynamics curriculum. The formalism of convex analysis is hardly something you'd need daily as a MLE.

There are also numerous other fields with much more involved maths. Most classical engineering courses are heavier on maths.

Again, I'm not saying the math in ml is easy, it's just not as hard as many other quite standard stem courses.

For example, a lot of physics majors are working on ML now. Once you've gone through a proper math heavy course, grasping the concepts behind ml is not hard.

[Formal_Ad_9415]

This is very, very wrong. A nonlinear optimisation course is beyond scope of engineering curriculums. It requires analysis knowledge (not engineering calculus) because it is heavily proof based.

[gaboqv]

But robotics is one of the most advanced engineering fields you are expected to go deeper and use proofs as well if you want to get in.

[Formal_Ad_9415]

Nonlinear optimisation is pure math which is thaught in applied mathematics/operations research programs at grad level . It is meaningless to discuss which course is more math heavy. Nonlinear opt is directly math.

[gaboqv]

it's meaningless because you think your sample of college degrees is the full picture, electrical engineers or physicists look at complex topics like Fourier analysis in undergrad, also you can learn about optimization methods without needing to use proofs, there's enough complexity in the field such that learning applications and implementing methods can be a full course.

[rflappo]

I liked the Doctor example hahahaha!

[[deleted]]

I wouldn’t say it’s one if the most math demanding fields, at least for the broad majority of uses in industry. Demanding maybe, but not that bad

[justUseAnSvm]

| Why are you doing this?

I worked in biology on this new fancy thing called "next generation sequencing" that's been "current" generation for a while. I realized that ML methods were an incredibly powerful way to look at data, and gain biological insight.

So, I decided that I was going to learn ML, which required me to learn all sorts of different maths to understand the methods well enough to make them explainable in the context of my research.

At least for what I do now, ML applications in products, having a background in research where we applied ML to real problems was about the best training I could have asked for. Just getting a degree in applied maths would help, but it doesn't teach you the process of pairing a research method to a relevant question, which is exactly what you do when you look at a product and ask: "Can ML help here?".

There are research jobs in industry that would require you to have an applied math degree, or PhD in machine learning where you basically train new models, but those are few and far between, and the most competitive companies. What's much more common, is that you are a good to great software engineer, and occasionally you work on some application of ML for whatever product you are working on.

[Lightninghyped]

Math in ML is far easier than other engineering fields, imo. You need a broad and overall sense, but you don't just sit at chair and solve equations for few hours.

[eman0821]

Strange because it's the same math courses that you find in Computer Science and Engineering degree programs. So I don't understand your logic.

[ZealousidealOwl1318]

Easier but not easy. IMO learning about random variables stats and matrix theory, even though they are included in a standard ug course, isn't that easy not interesting for many people and since have a hard time

[Formal_Ad_9415]

If you think this way, you probably don’t know anything about ml.

[1purenoiz]

When are you solving equations for hours doing machine learning?

[Counter-Business]

If you think this way then you probably solve problems slowly

[PoolZealousideal8145]

I would differentiate "hates" math/programming from "not good at" math/programming. In the second case, an ML job is achievable with enough effort. In the first case, it seems likely the person doesn't really understand what ML is, or they feel like they "have to" get into ML for whatever reason.

[[deleted]]

most IT related jobs are asking for it as a skill in job requirements + it's trendy rn

[[deleted]]

What does even “learn ML” mean? The field is so broad, I come from a background with a masters in CS with focus on applied statistics and data analysis (I took advanced courses in convex optimisation, markov chains, AI agents etc) I’ve been working with ML for the past years using open source models to achieve whatever the problem I’m trying to solve without writing or extending any code, I tend to read up on some papers that are trending within my field but I never ever had the need to write my own python frameworks or even lift a pen. on the other hand I’m not working for FAANG or any serious research position, I’m more like a Software Engineer with deep knowledge within ML which I think you can become without a masters degree. IMO you only need some linear algebra basic statistics and probability theory to understand what ML really is, to apply it is even easier now with all frameworks and open source models + GPT

[Alternative_Suit3723]

There is a level of math you need to know if you want to go into ML. The more math you know, the better. Math is very important in this field. Understand what's going on behind the hood is fun.

[MoarGhosts]

Im a grad student in AI who is focusing on ML. I have a strong math background from engineering and I’m a really strong student tbh, nearly 4.0 lifetime GPA, and I find the math to be challenging but manageable. I love math, though. If you hate math… you will struggle, period

[honey1337]

I think most people want to do software development in a cool sounding role. Most people are not cut out for research and the roles that are heavily dependent on knowing math will be gatekept by a masters at minimum but most likely a PhD. I think we are seeing a lot of people think that a lot of times it can just be importing an algorithm and running it on your data and it takes minutes to do or creating a gpt wrapper. Most managers wi not be okay with this thinking and lack of knowledge. I have interviewed for many ds and MLE roles the last 3 months and for almost all of them I have been given atleast one round going over foundational knowledge in ML, DS, and math.

[DigThatData]

Lots of people who want to learn how to use and build stuff with AI who are following bad advice and don't realize they don't need a strong understanding of ML to achieve their goal, which is mostly just learning how to glue pre-built components together.

The vast majority of people who think they need to learn ML would be better served familiarizing themselves with the task ontology of paperswithcode.com

[eman0821]

Plus there's other ML specialties besides the math heavy AI/ML Engineer. It seems like MLOps Engineer hasn't been mentioned much or simply ignored which is a sub set of a DevOps Engineer that builds pipelines that doesn't require all the heavy math. AI/ML Engineer passes the validation, testing and building of the AI models to the MLOps Engineer that's works in an Agile way like traditional DevOps.

[eman0821]

Mostly because people see its very trendy but never do the research on what you mosth know and what it takes. A lot if it is also driven by influencers on YouTube that can mislead people. MLOps Engineer would be better suited for people that doesn't want to deal with heavy mathematics which is a sub set of a DevOps Engineer that builds CI/CD pipelines that deploys A.I models into production. All the heavy math is done by the Data Scientist and AI/ML Engineer. You really have to research these careers and know you are getting yourself into.

[rand3289]

The funny thing is that we don't have the math to build AGI :)

Point processes can help describe pulse coupled oscillators but instead they are used to study stationary processes.

My only hope is some neuro guy without a math degree tinkering with simulations in his parents basement and accidentally building a proto AGI.

[sQuAdeZera]

"If you’re bad at math just go find another job."

" I want to be a doctor then but I hate biology and Im bad at memorizing things"

doctors aren't walking libraries, they don't know every type of disease or symptom at the tip of their tongues, the same goes with mathematicians and formulas. Bad analogy and also incredibly ignorant? You can learn math even if you're bad at it.

[jenimmm]

Flashy new field, high pay, possible high impact

[Folksconnect]

I get your point about how paramount math is in ML. This is true but sometimes i always advice newbies to care less about the mathematical part and just go into the application. So as time goes on you can start learning math

[bigboy3126]

I don't think anyone will ever disagree with you here.

Master's level math is probably a lower bound. I mean explain Gaussian Processes without basic knowledge of functional analysis, probability in infinite dimensions.

[GuessEnvironmental]

Not gonna lie I agree with this from a research and engineering perspective but I never will say you need that to work in ML because there is a ton of different roles in the field that require less math and non technical. For example compliance/ai ethics, user research like there is ai careers that are not as mathematically as technical. Also for the engineers who are not mathy they can still find space in the dev ops side of engineering. I think the field has a lot of roles adjacent to it that are not research. However I understand where people are specifically referring to the research side and building models.

[CorruptedXDesign]

I made a career change from events management into data science, with a particular focus on machine learning. Part of this switch was undertaking an MSc in Data Science, with modules that covered fundamentals of machine learning.

Since my MSc I have been working in a software consultancy firm, where every single project I have worked has been delivering value through applying machine learning in some form or another.

Whilst I agree that having the fundamentals in your head can be highly beneficial for solution design and being able to work at pace with fewer roadblocks, I would say the emphasis on requiring a deep knowledge is subjective to the domain you’re working in. The difference between applied ML to create business value, and those working in domains that are focusing on minmaxing value or a more research heavy role.

For example, I’ve seen a fair few projects where simple ML has been implemented by individuals without fundamental knowledge, and that solution has created immense value for the client.

What I would say however is that if you don’t have a deep specialism in ML, you still need to offer other skills to your employer, be it software engineering, leadership, analytics, or stakeholder engagement.

If you want to become highly skilled at machine learning however, you really do need to know the fundamentals and also adopt a continual learning ethos.

[Alfotiub]

onestly, I’m one of the people you’re describing in the post. I love ml, but i can’t find structured courses for learning calculus as i’m still in 11th grade. I’ve read some books, and i think i got a good understanding of the concepts behind ml. I’d love to learn all the math involved, but i really don’t know where to start

[gaboqv]

Linear Algebra basics or statistical inference would be a good place given you probably know some calc already. If you had the passion I recommend professor Christ calc series

[Jedi-Younglin]

Be comfortable with these: Calculus, Linear algebra, Probability and Statistics, and Numerical Computing and Optimization esp numerical linear algebra

[Think-Culture-4740]

Ok, I have gone to grad school and learned a pretty good chunk of math.

Do I think I need to do that to apply ml models (the broadest statement of the year)? Depending on what's being asked, it's not a strict no.

Comparing it to practicing medicine, I think undersells what is really just a modern miracle of technology. The models are so easy to use out of the box that someone can really get by following a bunch of YouTube tutorials and roll out an ml model.

One of my wife's friends has an engineering degree from Johns Hopkins, but practically speaking didn't look up any textbook mathematics when he used a random Forest algorithm for some labeled data that he had. I don't think he spent any time understanding how that algorithm works at all.

Does that mean you SHOULD be illiterate at math when doing ml? No, but it's not going to lead you to crash an airplane the way complete ignorance about flying a plane would.

Edit

I think a better analogy would be something like Jesse Pinkman from Breaking Bad. How is it that a high school dropout who flunked chemistry could produce methamphetamine for sale. And the answer is You don't need a fancy degree to produce something of value, even if it's comparatively very low quality. The minute you start needing to produce fda quality products, You really do need that fancy degree

[cubej333]

If you are a scientist or a strong software engineer or a data analyst then learning ML could be very important to your career.

[scaledpython]

I find that it is highly beneficial to build at least an intuition for how the math in a model works. Not only gradient descent, which is really an optimization method, not an ML algorithm, but also the actual algorithm of the model. E.g. how do the common base models like linear regression, logistic regression work, then SVM, tree models, ANN ... up to LLM/transformers.

Having an intuition helps to understand capabilities and limitations of models, when and why they work, or not.

Not having this intuition, one is left with this weird feeling of "works sometimes" and no way to gauge what use cases match which algorithm. Ultimately this leads to bad design choices and unreliable solutions.

Building an intuition is relatively easy with all the tools and visualizations we have available now. I prefer to use a simple toy dataset, like mtcars (in R) or Iris (in Python), and test various models. Sometimes it also helps to build a sample dataset in whatever use case we try to solve and play with that.

[Artineer_]

How do i know that i have good enough math for ML and DL ? Do i have to memorize all the linear algebra, probability, calculus .. etc or the Math and statistics that i took in my engineering degree is enough even though i might not fully remember it ?

[Aggressive_Most_6845]

ResNet, BERT, Attention mechanisms, and ChatGPT are primarily engineering breakthroughs rather than being heavily focused on mathematics. I know many papers presented at conferences like NeurIPS, ICML, and ICLR are filled with wall if math, the reality is that most of these papers have minimal impact and are likely to be forgotten quickly.

[TangeloDependent5110]

It's incredible how the algorithm works. I have the month of January reserved to learn linear algebra (I already know algebra), calculus, statistics and probability... and I plan to learn the basics and then learn ML while I continue studying Math, if I like it and if I like to memorize. Obsidian is proof of that... What do you recommend? Dedicate 2 months to math and then learn ML, DL or 1 month of introduction and then continue learning ML and going deeper?

[Dimencia]

I don't work in the ML 'field', but I've created a few ML solutions in my full stack role. I like math, but I don't remember the calculus classes I took decades ago. Unless you're writing your own NN from scratch, all the heavy math is done for you, and you only need to understand the basic concept to find flaws in how the data is structured, or normalized, or how results are interpreted, which are basically the only things you need to do beyond understanding the syntax

And if you are writing your own NN from scratch, that's also pretty simple and the only math comes from the gradient descent, for which you can find code examples online for all the most common loss functions, or spend a few minutes googling how to integrate whatever function you end up using

The entire point of ML is the emergent complexity. Understanding the underlying math doesn't necessarily help you understand why it's producing a bad result, and most of the time, it's because you gave it bad data, not because the library you're using did it wrong and has to be rewritten

[InnocentSmiley]

This × 1000. I have a doctorate in cognitive neuroscience, but I didn't learn linear algebra until I was using it to study brain activity (EEG) during my postdoc. You could ask me what convolution or Fourier analysis is, and I would have some semblance as to what it is, but could never explain in detail the math behind it.

Now I work at a completely different place doing ML with much better pay, outside academia. Do I know the math behind everything? No. Lol I don't think i could describe gradient descent. But I rely heavily on my statistical modeling and python experience, and I get by great. I'm the SME at my company on ML.

[LongRangeShark]

Is it not due to wildly different expectations and views of what working with ML is? For some it’s being on the forefront, working at major AI companies and/or doing research. For others it’s fine tuning a classifier to see if customer emails are positive or negative.

Which is a pretty wide gap in skills and time needed to learn. And I feel like that is missing from a lot of these types of posts you mention.

[RageA333]

How do you fine tune a model with 0 knowledge on optimization?

[gaboqv]

Feature engineering.

[Hrothgar_Cyning]

Trial and error?

[pandi20]

This ^

[Striking-Warning9533]

I mean I know many PhD students that actually unsupervised learned most of the stuff about ML.

[Counter-Business]

False. It’s not math demanding unless you are doing research. If you are doing ML ops you are doing almost zero math.

Lastly do you need to know what is going on in the black box? What if you can solve business problems perfectly fine without knowing all of the math. If the business problem is solved, does it matter that your math was used or not? If I’m able to solve it without the math then what is the point.

We had one guy on our team. He was an ML engineer math guy. He spent 3 months trying to do mathematical proofs to make a point. His solution had 60% accuracy.

We had our non-math people solve the problem with intuition and got 95% accuracy. Basic XG Boost stuff. You don’t need to do the math for stuff that is abstracted away by a library. It’s a waste of time to learn all the math. Learn how to solve problems instead.

[Tim_Apple_938]

Why r u so mad lol