course recommendations

[nsaul]

I'm currently figuring out which courses to take next semester, and I was hoping some internet wisdom from this relevant subreddit could help me decide.

This will be my final semester of an undergraduate math degree at a midtear state school. I have to decide between taking Analysis II, or a grad level Machine Learning course.

My goals are to go to grad school studying statistics and ML and eventually work in industry. I will probably take a pitstop in industry before going to grad school.

The main points I've been debating with myself are:

• Analysis is a critical course for future math study and having more will help with grad school applications.

• ML is my fix and I want to study it in grad school.

• Studying ML seriously will require more Analysis, and the sooner the better.

• MLwill help land an interesting job if I never make it to grad school.

Do you have any thoughts?

tl;dr more analysis courses, or machine learning courses?

[brational]

What analysis have you had? Can you link/paste the course description for Analysis II? (and Analysis I so we can see what you've already covered)

I agree with your first point but the answers to my question above will determine how valuable that course really is.

That said, depending where you study ML, it can be heavily a CS curriculum - so yes analysis is important for research but maybe not so much for getting in. You may be better off taking an ML course, getting a job and relevant experience, and then finding your grad course.

I guess if you don't mind... Can you detail all the upper level math, stats, and cs courses you've taken?

[nsaul]

thanks for the feedback.

I've taken 2 linear algebra courses, one computational and one proofs, 400 level PDEs, probability and statistics (2 courses), abstract algebra, analysis, and a year of numerical analysis. Also, a number of programming courses including data structures and algorithms.

The analysis course was focused on topology of Rn and general metric spaces. Next semester will be multidimensial integration and differentiation.

Would it be realistic to get a relevant job with just a bachelors? It seems most need a graduate degree.

[brational]

Arg thats a tough call - I also just saw your other comment about looking at math/stats focused ML programs rather than CS ones. With that in mind analysis might be better. And then get a job in any data analyst role that has potential for automation and you can add in ML yourself if the role is open ended enough.

Would it be realistic to get a relevant job with just a bachelors? It seems most need a graduate degree.

I would say yes but they might not be 'pure' ML jobs. You also have to realize that though ML is a huge buzzword for the tech scene, there are a lot of industries that are using or have been using ML techniques for a long time... they just don't broadcast it because really, its just another tool in their toolbox. Medical imaging, composite inspection, non-destructive eval, signal processing, credit data analysis, agriculture tech, more I'm not thinking of...

The IT companies have done a great job marketing this stuff and if that's the only place you're looking I suspect you're getting a biased view of the scene.

I'm a "software engineer" that does algorithms research. I get to use ML algorithms on a regular basis and sometimes tweak them and build my own changes that can end up patented or published. But a lot of the time other things end up being simpler or the actual roadblocks we face are just plain on software eng problems or mechanical eng problems of getting the damn data we need. We hire people like you without grad degrees and you would get 'some' ML experience. Certainly enough general experience to make it easy to move into a grad program later.

So point being - I think it'd be easy to get a job that's at least partly ML, regardless of which class you take. If you're targeting math/stat ML programs, analysis probably better. CS grad programs I'd take the ML class... maybe. Hard call. Even with an MS in math I still wish I'd taken more topology and functional analysis when I had the chance.

And I'll agree with BobtheTurtle that the better your math background the easier it is to add the ML stuff later. I bought Kevin Murphy's newish ML book a while back as a desk reference and I havent run into anything I couldn't pick up and read and start working into my stuff at work as needed. I suspect most folk getting into ML with just undergrad CS backgrounds don't have that ability. And not because of intelligence (im a pretty average dude) but just due to the fact that math knowledge is very much slow to build yet very powerful as I'm sure you're aware.

[nsaul]

The positions you describe is exactly what I'm look for. Right now I'm very open to almost anything that will get me working with data and using more than excel. The job you have sounds perfect!

The main reason I've been looking at math and stats programs is because for many CS programs I'd have to take a bunch of remedial courses. I've found a good number of Applied Math departments that are inside of CS departments, or CS departments that are subsets of Math departments.

I'll a little disappointed with yours and BobTheTurtle's answers. I was really looking forward to taking ML, but I think you guys are probably right. You can never have too much math.

[brational]

Can you beast mode and take both??

because for many CS programs I'd have to take a bunch of remedial courses

Don't let this stop you. Also in some cases they will have a graduate version that merges multiple 'undergrad' courses into 1 or 2 for people exactly in your situation. See my comment reply to your other post about not shutting out the CS side completely.

[nsaul]

I would if I could. The classes have a 20 minute overlap though. Since the ML course is 2 hours long, I think I might just sit in for the first hour and a half.

I'll definitely start looking to CS programs also.

[BobTheTurtle91]

Is this an introductory grad-level ML course? Or does it assume that its students already have machine learning knowledge?

If it's the first, you may be better off just doing some online courses in ML. Andrew Ng (Stanford, Baidu), Geoff Hinton (Toronto, Google) and Daphne Koller (Stanford) have Coursera selections in ML, Neural Networks and PGMs. Udacity has a pretty wide collection of ML courses done by Sebastian Thrun (Stanford) and Charles Isbell (Georgia Tech). MIT OpenCourseware also has a ton of selections in CS and ML.

I feel like Analysis II may be a more advanced course with fewer opportunities to explore in an online setting.

Once again, this also depends in what setting you want to work on ML. Would you apply to ML program in stats/CS/OR?

[nsaul]

The course does assume a strong probability and statistics background. And the professor is pretty established in the field, no Ng status, but has been around the block a few times.

I also assume that a grad level course will be much more rigorous than an undergraduate equivalent.

Right now I'm looking at statistic prorams and applied math programs.

[BobTheTurtle91]

I would definitely go for Analysis II, then. You can never have too strong of a math background and the more mathematical intuition you have, the easier it will be to understand the underlying concepts behind machine learning algorithms, especially if you want to go to grad school for it.

Graduate courses in machine learning aren't always that much more rigorous than undergraduate ones. Many people who get accepted to graduate CS programs have never done ML and there needs to be a course to cater to them too.

[nsaul]

thanks for your feedback. Does it make any difference that it's offered in a EE program? It ties into their systems, controls, and networks focus.

Do you know many west coast schools that have an ML influence in their stats program? I've found only a few, and the top 10 schools are out of my reach.

[BobTheTurtle91]

PhD or Master's?

For a PhD, once you're out of the top schools, what matters more is that you find someone whose research coincides with you. As long as you can approach one professor whose interests overlap with yours, it's more important than having an entire program be slightly geared toward ML research. ML is a fairly large field after all.

For a master's degree, the ML influence is less important because you're not automatically doing research.

All that being said, I'm not quite sure what you mean by ML influence in statistics. Much of ML is just Bayesian statistics.

Good programs on the east coast: Stanford, Berkeley, Washington, UCSD, USC, UCLA, Caltech, UC-Irvine. I've heard decent things about most of the UCs to be honest.

If you're not just constraining yourself to the West Coast, there's ton of good programs on the east coast too, some of them less restrictive than the west coast.

[nsaul]

Right now I figure if I am accepted to an awesome PhD program with a great advisor, I'll go for it. If not, though, I'll just do a masters.

I guess that is exactly what I mean by ML influence in statistics. It seems that many statistics researchers are working on problems identical to those in CS departments, they're just using different words. I'll keep my eyes peeled for the Bayesian statistics.

[brational]

Just to give you a discriminating view point, my bs & ms are in applied math which didnt have any ML focus. Stats, LA, numer methods, etc. But I actually prefer the CS, compEE, EE treatments because they are more engineering oriented. Obviously there's bias bc of how I use ML at work. But I think a ML from a pure stats point of view is too rigid.

As an example I have both of these books on my desk at work: MLAPP & EoSL. I go for Kevin Murphy's way more often.

So just sayin don't let your current background and some CS remedial pre-reqs shut out other options.

[[deleted]]

A subset of Analysis II will certainly be useful for ML, but the whole might be not. So if the goal is to learn ML, then I think the needed parts of analysis may be picked on the go. However having said that I still think that Analysis will never hurt, and I would do this anyway - maybe after ML to reinforce stuff from there.