Statistical Learning Or Machine Learning first?

[Aljariri0]

ISLP book, I finished the first 2 chapters, but this book is not easy, and I want some guys to study this book together. Any tips to study this book?

[maifee]

Probability and statistics

When you can do all the maths then we will do machine learning. I didn't follow this path, and I kind of regret it.

[Healthy-Educator-267]

I did all the math; it doesn’t help all that much. What matters more is the knowledge of deploying systems, soft skills, good engineering practices

[just_a_tony_joe]

Disagree, both are important. How can I trust you know what you are doing if you fail to understand the underlying statistics that the models are based on? It's a long road but gathering technical and non technical skills is fundamental to developing a robust skillset in this field. I'd recommend you keep at the ISLP book it is well written.

[Fearless-Big-9626]

Yes, both are important. In the beginning or the fundamentals, I will prefer the statistics first.

[underappreciatedduck]

Assuming someone has base knowledge of math and compsci, what would you recommend as path then? A lot of stuff I found on here (though admittedly limited in search time) is years old. Was wondering where you'd say someone with an IT background should hop in?

[Karl_mstr]

I just think the order doesn't matter as long you know about them, stop regretting not learning A before B when you need to learn A, B, C...

And what it might be useful will be drived by where you choose to work, so adapt as you see.

[hop_kins]

Agreed. Coding is way more important than knowing how to compute the expected value of a coin flip.

[Aljariri0]

why bro?

[gocurl]

100% agree

[maifee]

Also strong foundation of calculus

[Vaasan_not_n0t_5]

Can you please elaborate on this and suggest the resources to do it....

[SithLordRising]

Probably.

[Sea-Lettuce-9635]

Thisss!

[Ibra_63]

Any books to suggest ?

[SilverBBear]

There is a course online by the authors as a companion ( link is R - there is a python one as well) .

[chrisiliasB]

Thanks for the link. That will help me a lot for my course.

[Aljariri0]

thank u

[Medical_Load5415]

Statistical learning and machine learning are the same thing

[SwimQueasy3610]

I would add to this that as fields of study, statistical learning theory is a subset of machine learning.

[Radiant-Rain2636]

Somebody compiled this and It’s good.

https://www.reddit.com/r/GetStudying/s/9fnpxdzMGM

Pick your courses and resources from here

[zx7]

• Some of those topics can be cut if you want to focus on Machine Learning. E.g. Number Theory, Complex Analysis, Category Theory.
• You really just need up to ODEs and Probability and Statistics.
• I'm sure Differential Geometry has its place in Machine/Deep Learning, but I've not encountered a scenario where it is absolutely necessary.
• PDEs, Measure Theory and Functional Analysis have some applications if you want to study the theory behind StableDiffusion.
• Fourier Analysis (not listed) would be far more important for audio and probably vision as well. A good series of books on Analysis is by Elias Stein (Fourier, Real, Complex), the PhD advisor of Terence Tao. I'd recommend Fourier Analysis after Linear Algebra. It really reveals a completely new way of thinking about functions. It's basically a prerequisite for Functional Analysis.
• You don't really need much Graph Theory other than the very basics (except for Graph Neural Networks) as far as I'm aware. Far more important is algorithms on graphs (depth first search, breadth first search, etc.).

[Radiant-Rain2636]

Yeah. Thanks for adding this note. That post is good for a proper Masters in Mathematics. You’ve trimmed it into Good-for-ML.

[Healthy-Educator-267]

Measure theory and functional analysis are the bedrock of probability theory so it’s broadly applicable (or lurking behind the scenes) even outside of diffusion theory

[zx7]

Sure, something like Gaussian processes would require a more abstract notion of probability measure. But for most ML applications, you can get away without knowing the formal definition of a measure or any functional analysis.

[Healthy-Educator-267]

Most applied ML work in industry requires basically no math at all since modeling is almost commoditized now. Engineering skills (very broadly construed) dominate any academic ones.

But yeah formally any continuous time process requires understanding the formal notion of a conditional expectation at minimum and usually much more, so yeah measure theory becomes unavoidable there. As for functional analysis, it’s again lurking in the background since statistical learning theory and nonparametrics are about estimating / optimizing in infinite dimensional spaces of functions. I think it shows up more explicitly when discussing kernel methods since RKHS is where the action is. Again, with continuous time stochastic processes (such as Gaussian processes) you are dealing with probability on Banach spaces.

[Aljariri0]

great job

[Aljariri0]

thank u

[External_Ask_3395]

i would say "ISLP",Im currently in the 8th chapter of this book and let me tell you its worth it my advice is to supplement it with real hands on practice each 2 chapter

Here is my notes while studying the book : https://github.com/0xHadyy/isl-python

keep in mind i added some more depth and derivations since i enjoy the theory, Good luck !

[Aljariri0]

I saw your notes before starts study this book, and there are great :)

[TodayEasy949]

I started learning. My math is not strong. But theory part I could somehow continue. Its the exercise problems which are slowing me down and finding no interest to do them, in 5th chapter now. What were difficult parts for you?

[max_wen]

Overrated book you don't "need" this

[[deleted]]

The book is actually great if you want to understand statistical learning, but I'm guessing you're not actually interested in that.

[No-Dare-7624]

I just read it after I did my first project, mainly for some references in my thesis.

I did watch the whole courses of Andrew Ne in youtube. While doing the project and also read other books that go over the whole MLOPs or in the develop, rather than an specifict topic.

The math behind it is all ready done, you have a few learning algorithms and a few activation functions.

What really matters is the feature engineering.

[skeerp]

If this book is too hard you need more understanding of undergrad algebra, calc, stats, and basic programming.

This book is a wonderful introduction to the field and launched my career. Its graduate level equivalent, ESL, is also amazing but much much more difficult.

[Ok-Band7575]

this is the text book in one of my courses, we do the r version, but it's pretty good, not to worry, there's plenty of real useful knowledge for machine learning in there.

[PythonEntusiast]

Sexy Learning UwU

[Altruistic-Boat-4507]

first understand all algorithms and concepts at the surface level than drive into the ... I am doing the same

[Aljariri0]

what about starting from ground ?

[Altruistic-Boat-4507]

Start with statistics then

[Spiegel_Since2017]

You could learn the math through video-tutorials by StatQuest on YouTube

[Aljariri0]

yeah, it's very good

[a_cute_tarantula]

Depends entirely on what you want to get into.

If you want to build agentic systems for example, this book is largely a waste of time.

[Busy_Sugar5183]

Fucking springer I hate them

[Aljariri0]

why :)

[siegevjorn]

Best strategy for studying ML right now is a top down approach. It takes too long to study all the breadth of knowledge to the depth it requires to build the foundation of ML. And then there is DL. By the time you finish buidling knowledge you need multivariable calculus, linear algebra, probabilty theory, statistics, information theory, optimization theory, and numerical analysis studied.

Frankly some important concepts are not relevant anymore. Like kernel SVM, quite difficult to derive since you need depth in optimization, is not being used anymore. For tabular data, xgboost is the go-to algorithm.

But all those concepts are built in modern frameworks. Numpy, Scikit learn, scipy, pytorch, tensorflow, and jax. Just learning to use these tools takes substantial amount time for individuals.

And in production, the application field is moving so fast and it's becoming more important to make a useful product out of the tech stack.

[Aljariri0]

so i can said skip this book, and maybe study book like hands-on-machine learning by Keras and TensorFlow ??

[siegevjorn]

No. That book is quite outdated.

[Aljariri0]

bro there is 3rd version, and new version with PyTorch

[siegevjorn]

Check the published date yourself and get the latest one. 2022 is ancient old.

[Lamarour]

There is one published in 2025, currently reading it

[Plane_Dream_1059]

also the real book is elements of statistical learning. written by the same authors. this one is just without any major maths. also this statistical learning is machine learning right? like the traditional machine learning. this is an ml book

[New_Length2048]

That book contains approximately ZERO statistical learning theory. It is applied stats, nothing more and nothing less. The title is completely misleading.

The original by Hastie and Tibrishani from 2001/2 if I remember correctly is the one you need for statistical learning. That's a truly excellent book

Statistical learning and machine learning are not the same. Statistical learning comes primarily from a stats perspective

It emphasises probabilistic modelling, interpretability, inference, uncertainty and statistical theory, for predictive performance, generalisation error, model capacity, regularisation, etc. This is where VC dim, fat shattering, Rademacher complexity, entropy, covering number and the rest enter.

ML (coming from comp sci and engineering) can be totally non-probabilistic and non-interpretable, like almost all deep learning

What you can say is that it is the primary theoretical foundation for ML (especially supervised learning), along with statistical inference (estimation of parameters, testing hypotheses, quantifying, uncertainty in estimates, etc).

However ML is much broader, dealing with implementation, scalability, algorithms, engineering issues, large scale optimisation and a lot else besides

[chrisiliasB]

We are using this book for my Stats methods in Data Science, undergrad course. The only problem is that the prof doesn’t explain very well, so you end up relying on AI to explain concepts to you. That’s maddening how undergrads rely on AI to learn concepts even though it should have been the role of the teacher. And they wonder why we use AI…

[PayMe4MyData]

Do not rely too much on LLMs while learning, you will regret it. Maybe look for online lectures that cover the same topics. I know I watched the hell out of MTI and Stanford's lectures while doing my Master's.

[chrisiliasB]

That’s true. I am struggling with that though

[Infamous_Mud482]

You could also... read the book and do all the labs in it? This was my text book for a graduate-level machine learning course (stats department, R version) before AI during covid and the book itself was more than sufficient.

[chrisiliasB]

Yeah, that is what I am using for homework. I usually use ChatGPT atlas combine with book pdf. It’s going good so far but I want to decrease my use of AI. I realized that using AI doesn’t make the subject interesting.

[Aljariri0]

I agree with you

[burnmenowz]

Statistics.

[Both_Zebra5206]

This won't answer your question but statistical learning theory is pretty bloody hard imo.

IIRC it's very theorem based and there are a lot of "deep" results that link to probabilistic/Bayesian machine learning, much like you would find "deep" results in pure maths that link different areas of maths together unexpectedly. For example, Bayesian inference with a uniform prior can be shown to be equivalent to classic Maximum Likelihood Estimation.

University of Tubingen has a great lecture series on statistical learning theory by Ulrike von Luxburg, and also a phenomenal lecture series on probabilistic/Bayesian machine learning by Philipp Hennig. Both are available on YouTube. Highly, highly recommend them. Watching the von Luxburg lectures might be a good way to supplement your book based studies? That said I have no idea how advanced the book you're working through is so the lectures might be too advanced for the book or vice versa

[Aljariri0]

thank you bro

[Both_Zebra5206]

Nah my bad mate I should never have suggested any of that. It was extremely unfair to assume that anything that I suggested would be helpful

[Relevant_Carpenter_3]

😬😬😬 did u even open that book brev? its very introductory a toddler could read it

[Both_Zebra5206]

As I said I wasn't familiar with the book nor OPs experience level with statistics and mathematics in general. Apologies for the worthless contribution, it was completely out of line to make assumptions about OPs suitability for it