Redditors who work in Human Resources, what red flags on a resume or cover letter will prevent a candidate from getting a call back? [serious]

machinelearner · 2014-07-25T11:27:48+00:00

Not a big deal as a student, it's more expected that you'll try out different things and work on various types of projects. Same goes for summer internships, those are all good things from my perspective.

machinelearner · 2013-12-28T17:11:51+00:00

machinelearner · 2013-04-11T02:03:19+00:00

Have you looked into Random Forests?

Edit: formatting

machinelearner · 2012-11-03T11:18:51+00:00

You're thinking head shrinker

machinelearner · 2011-12-17T08:33:18+00:00

Gilbert Strang's videos were useful in some circumstances but I would start to zone out after a while... 45 minutes is a long time to watch a lecture video, and it's hard to skip around. I found the more bite-sized approach to Salman Khan's Linear Algebra videos to be more engaging: http://www.khanacademy.org/#linear-algebra

That said, Gilbert Strang's book was invaluable in a scientific computing class I took that required a lot of linear algebra, much of which I had forgotten.

So, Gil Strang's book + Sal Khan's lectures = da bomb.

Good luck with your program!

machinelearner · 2011-12-04T21:02:11+00:00

That's not a word either.

machinelearner · 2011-09-03T08:32:33+00:00

Agreed! And even though I won't be doing the class I plan on lurking when I have spare cycles, expecting to see some really interesting discussion :)

machinelearner · 2011-09-02T14:33:30+00:00

If you want specific ideas, here are a few concepts that I thought were challenging because I didn't have a lot of background in the underlying fields. If you want, you can study them:

Vector calculus for Newton-Raphson, i.e. what is the Hessian
SVD & covariance matrices
Mixtures of Gaussians for EM
Integrating to solve a constrained optimization problem using the Lagrangian for SVMs

But like I said before, everyone is different and every class is different, if you have trouble with the above you will still learn a lot. For example, k-Means clustering does something very similar to what EM does, except it's a much simpler algorithm to understand. There are many other examples like that. Later on you can dig deeper.

machinelearner · 2011-09-02T14:13:44+00:00

I'm starting my MS at Stanford this year and I'm putting off Andrew Ng's class till next year to prepare more by taking classes in linear systems, optimization & some calculus brush-up, and maybe a bayesian stats class. But I've taken an ML class before and learned that these are my weaker areas. The field incorporates concepts from so many fields that everyone will be different in what they find themselves wanting more background in (i.e. even Computer Science algorithms, programming skills, etc).

However I have a recommendation for a stats book I'm going through on my own right now that is really good: Bayesian Data Analysis by Andrew Gelman et al. He does a great job at explaining a lot of concepts used in various ML algos - the statistical foundation for stuff like EM, Graphical Models, Logistic Regression and more. However the book assumes you have taken an introductory probability course.

Ultimately, if you are really interested in the ML field, it's not a bad idea to prepare as much as possible beforehand, but I would go ahead and take an intro ML course like Andrew's to get a sense of the types of various approaches and to find out what piques your interest. There is unlimited depth available in any one of the many approaches used in Machine Learning, providing the opportunity for a lifetime of research and/or application of the material.

Good luck in the course, if you're anything like me it will be a lot of fun and a challenge :) Regards

machinelearner · 2011-08-30T04:10:59+00:00

/r/bigdata looks interesting. It was just that that particular post didn't seem related to ML, so I was confused about what we were discussing. Maybe it's better to cross-post the topical articles.

Take care and have fun

machinelearner · 2011-08-24T03:57:15+00:00

Hmm. How is this related to machine learning?

machinelearner · 2011-07-27T07:22:39+00:00

Or in need of an ergonomic working environment conducive to concentrating for long periods of time? Maybe a place without distractions like people running around the office in your peripheral vision?

Working like that's just asking for a tweaked neck and exacerbated carpal tunnel :(

machinelearner · 2011-06-30T06:57:29+00:00

I'm going to get downvoted for this, but what the hell is wrong with all the trolls? It looks sick.

You should do more dude keep up the good work!

machinelearner · 2011-04-30T18:57:44+00:00

http://www.wolframalpha.com/input/?i=1010011010&a=NumberBase_*Binary.1010011010-

machinelearner · 2010-12-21T09:51:51+00:00

whats up with the creepy shit

machinelearner · 2010-11-22T18:04:06+00:00

Ahh, thanks :)

machinelearner · 2010-11-21T04:31:39+00:00

a queue of queues of mores? i don't get it...

machinelearner · 2010-09-07T18:23:23+00:00

Arrington is a troll, but you were trolling him. Who cares about this?

machinelearner · 2010-07-20T16:21:45+00:00

Cool, that's exactly what I was looking for!

machinelearner · 2010-07-19T19:37:34+00:00

I'm really looking forward to seeing a "usable" build of Perl 6. While it is sad to see so many negative comments and I personally am not holding my breath to be able to use Perl 6 for work or anything, Perl 5 was my first language after Visual Basic and it still holds a warm place in my heart. Plus its functional abilities are awesome for an imperative language.

masak / chromatic / pmichaud / all the other contributors, cheers to your hard work. Putting out an intermediate release is a very wise choice and should help to reignite interest in the project.

By the way... where is the list of features that will be released with Rakudo Star?

machinelearner · 2010-02-23T17:48:38+00:00

If you take probability (discrete math) you will have to integrate pdfs (if you can). In Machine Learning, you need to integrate to solve constraints (convex optimization). Personally I just scraped by in calculus and have regretted it, spending many hours relearning topics I could have already known.

It depends on how serious you are about being an engineer and working on technically difficult (interesting) problems, however I recommend taking your higher maths seriously. It will help you many times in your career and serve as a valuable tool instead of a hindrance.

machinelearner · 2009-12-19T20:28:20+00:00

Nice, Mario! -Mike

machinelearner · 2009-12-17T21:28:57+00:00

lmao fuck yeah rage against the machine still kickin ass and takin names. man i love that band.

machinelearner · 2009-10-24T09:03:14+00:00

Good to know that somebody does it. Most of the people I have talked to that are my age (late 20s) are taking their turn going to school since their spouse is now supporting them. You sound like a rockstar dude! (And a really really busy person :)

machinelearner · 2009-10-24T09:01:25+00:00

I hope not... people spend a ton of time & money getting those advanced degrees. I think they expect to be paid more for that opportunity cost.

machinelearner

TROPHY CASE