Is this type of math optimization?

Any MS level statisticians who have moved into ML research?

The whole landscape of ML research is a hunt to chase SOTA by tweaking an architecture here or using a different optimizer there and then squeezing out 0.2% accuracy on some well known imaging dataset in an attempt to churn out papers. That’s not science if you ask me.

Stats is finding interpretable ways to look at and mode data that ML plug and chug cs people don’t do

Statisticians care about inference. ML scientists care about the model specifically.

Sorry, I only speak Bayesian.

How would you recommend a student like me, whose a phd student in statistics, to be marketable for industry related ML research? I’m worried that in my time as a phd statistics student, my work will be too “classical” and “foundational” and lie more in the statistics domain rather than ML, and not be attractive for recruiters in the ML research space. How would you advise myself to be come off as more of a ML researcher than a pure theoretical statistician? Just focus on more ML related applications in my research?

I see

Could you elaborate

I have one minor gripe about deep learning textbooks. I think they are great references, but should not be used as a way for beginners to get into the field. I genuinely feel like time is better spent on the student going down a rabbit hole of actual papers of maybe one of the chapters of those books, say, a student reads the chapter on graph neural networks and the proceeds to read everything in graph neural networks, rather than read the whole book on different subsections.

Have some questions regarding multivariate time series classification.

I have some data. experimental data where there are three measurements measured at each time stems. Each is a univariate time series (Xposition, Yposition, roadoffset, brake)

And I need to predict the response, which takes 3 values.

It’s a classification problem, and i have read literature on the various methods. I have tried distance based methods, and decided against it because the distance matrix is too costly. I have got some code with sktime library on using the methods with convolution kernels (ROCKET, ARSENAL), and some ensemble methods (TimeSeriesForestClassifer).

However, I thought of a different method myself, where I split the data, into 2 second intervals, leading to 10 chunks of 2 second intervals of data. Each chunk is roughly 3000 samples.

Now that I have these 10 chunks of data, I wanted to extract some features from each of these 2 second intervals for my multivariate time series, and use these features in a classification model.

My hope is to do this so I can capture some temporal specific features. However, I don’t know what would be useful features to extract. What would be some useful features to extract from such a series?

I was hoping to get feature vectors from each of these intervals with my response in order to construct another classifier. Or are there methods that are out there they already do this?

BARTS (Bayesian additive regression trees). Also just in general ensemble learning is a useful approach to advance modeling in other areas with different kinds of data. For example, the area im reading about now which is time series classification has a ton of literature on time series models using ensemble learners under the hood. For example check out models like Arsenal, ProxmityForest, or other ensemble based methods for time series classification

Gotcha. Thanks!

Thanks a lot of the advice. I’ll do some more research on 1), as I’ve applied to schools but I’ve done little screening on the “vibe” of potential advisors and labs.

I’m an undergraduate. I want and aspire to be in a position like yours. I want to be you. Like my career dream is yours. What can I do right now (applying to PhD programs in statistics ) to get to your spot. What advice do you have for undergrads if they want to break into your field of ML research. I know what I want to research, i have the books and resources and the classes to take. The professors and classmates I will meet in my PhD program. How can I end up in your position.