41–50 of 57 posts
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Linear regression is ML. You are off base.
That's cool. Can you explain what machines they were using in the 1800s to do learning on?
All of these are machine learning techniques. Doing it by hand doesn't change anything. Today we use machines so it's machine learning
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Linear regression is literally the second lecture of the Stanford ML class. https://cs229.stanford.edu/ If you want to say "not neural networks" or not dnn or not llm, sure. But it's obviously machine learning
When you say it’s “obviously machine learning”, how could that statement possibly be correct? There’s not even broad consensus here… so you don’t get to say that your definition is obviously correct. There are pedagogical reasons why you’d include linear regression in a machine learning course. This is pretty clear to me—they have properties which are extremely important to the field of machine learning field, such a…
> Linear regression is also a type of machine learning algorithm, more specifically a supervised algorithm, that learns from the labelled datasets and maps the data points to the most optimized linear functions that can be used for prediction on new datasets
You can pretend it's not because it's not a sophisticated machine learning algorithm, but you are wrong.
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If you want to draw the line between ML and not ML, I think you’ll have to put Kalman filters and linear regression on the non-ML side. You can put support vector machines and neural networks on the ML side. In some sense the exact place you draw the distinction is arbitrary. You could try to characterize where the distinction is by saying that models with fewer parameters and lower complexity tend to be called “not…
Linear regression is literally the second lecture of the Stanford ML class. https://cs229.stanford.edu/ If you want to say "not neural networks" or not dnn or not llm, sure. But it's obviously machine learning
Am I missing something or is this just "We built models that are bad"?
I work in the ICU monitoring field, on the R&D team of a company with live systems at dozens of hospitals and multiple FDA approvals. We use extended Kalman filters (i.e. non-blackbox "ML") to estimate certain lab values of patients that are highly indicative of them crashing, based on live data from whatever set of monitors they're hooked up to - and it's highly robust. What the authors of this paper are doing is th…
Calling EKFs "ML" is certainly a choice.
Earlier quoted context omitted.
Linear regression is literally the second lecture of the Stanford ML class. https://cs229.stanford.edu/ If you want to say "not neural networks" or not dnn or not llm, sure. But it's obviously machine learning
When you say it’s “obviously machine learning”, how could that statement possibly be correct? There’s not even broad consensus here… so you don’t get to say that your definition is obviously correct. There are pedagogical reasons why you’d include linear regression in a machine learning course. This is pretty clear to me—they have properties which are extremely important to the field of machine learning field, such a…
Linear regression appears in pretty much every ML textbook. Can you confidently say, "this model that appears in every ML textbook is the only model in the ML textbook that isn't an ML model"?
Kalman Filters are like a continuous-state HMM. So why are HMMs considered ML and Kalman Filters not considered ML?
IMO it's an ego thing. They spent decades rigorously analyzing everything about linear models and here come these CS cowboys producing amazing results without any of the careful rigor that statisticians normally apply. It's difficult to argue against real results so the inflexible, hard-nosed statisticians just hang on to whatever they can.
I work in the ICU monitoring field, on the R&D team of a company with live systems at dozens of hospitals and multiple FDA approvals. We use extended Kalman filters (i.e. non-blackbox "ML") to estimate certain lab values of patients that are highly indicative of them crashing, based on live data from whatever set of monitors they're hooked up to - and it's highly robust. What the authors of this paper are doing is th…
Parameter estimation is ML now?
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Is it less ML than linear regression?
If you want to draw the line between ML and not ML, I think you’ll have to put Kalman filters and linear regression on the non-ML side. You can put support vector machines and neural networks on the ML side. In some sense the exact place you draw the distinction is arbitrary. You could try to characterize where the distinction is by saying that models with fewer parameters and lower complexity tend to be called “not…
Anything that can separate data apoints can rightly been seen as a "supervised machine learning classifier".
Todemystify the area, I literally introduce my intro to ML lecture by drawing a line on the board, give its equation y = 0.5 x on the backboard, reminding students that they already know this, and then explain how to use it as a spam filter by interpreting the points on either side of the line as good emails versus spam ones.