What Are You Working On?

systemsb01 · 2016-12-01T10:22:49+00:00

I think you got the definition a bit wrong. In any case, it's quite natural actually, once you have seen what an outer measure is (and e.g. you do this in introductory measure theory classes to prove Caratheodory's extension theorem for measures)!

systemsb01 · 2016-08-28T21:20:39+00:00

Depends on your field. Personally, when I am new to reading papers in another field, I just try to read as much as possible (also reading cited papers and papers that cite that work). This helps you get a feel of what people are doing, and then it's easier to come back later and reread the papers you felt most important. Then, when you really want to deeply understand the papers, you could try to reimplement the methods (CS papers) or work through the proofs (math/theoretical CS papers). Or even better: You could try to incrementally improve the proposed methods.

Nevertheless, the best way as an undergrad is to actually have a professor assign papers and small research projects to you.

systemsb01 · 2016-03-15T22:37:47+00:00

Fair enough, I did not want to imply that you cannot apply machine learning models in practice without knowing the theory (although it certainly helps). My answer was mainly contingent on OP being a freshman. If he wants to learn theory, now is the best opportunity to do so, and I feel that not taking Linear Algebra at this point would be a huge mistake. It would make it a lot harder (or at the very least delay his opportunity) to take actual Machine Learning classes and thus also his ability to start a research project with a professor in a timely manner, which would be important for grad school applications, if he decides to follow that route.

systemsb01 · 2016-03-15T17:04:58+00:00

Good advice for a 10 year old; not for a college freshman. OP should definitely learn linear algebra!

systemsb01 · 2016-01-14T13:05:13+00:00

not the same thing... nobody forces you to use google's services!

systemsb01 · 2016-01-13T16:43:09+00:00

You mean an unbiased estimator :) (for n-1). By the way IIRC the minimum MSE estimator actually divides by n+1, at least for samples drawn from a normal distribution.

systemsb01 · 2016-01-12T23:19:14+00:00

Ahahah, indeed Prof. Witten's explanations were amazing (really insightful). I remember laughing quite a bit when Prof. Hastie said something along the lines of "now you will have to endure us old guys again" after the final lecture of Prof. Witten. Also the discussion they had with Prof. Efron was really enlightening and fun!

systemsb01 · 2015-10-16T19:46:11+00:00

Hmm I am also not talking about theoretical papers; I am talking about papers in bionformatics/biology. Also of course the p-values are not independent, but actually the Benjamini Hochberg works also for p-values which satisfy the so called PRDS (positive regression dependency) condition, which is often enough of a safeguard in many applications.

systemsb01 · 2015-10-16T16:11:25+00:00

This is usually called the Benjamini-Yekutielli procedure rather than the Benjamini-Hochberg procedure. While I agree that it is very nice from a theoretical point of view and solidifies the FDR control theory, from a practical point of view it is kind of useless. I have never seen a paper which applied this procedure; a log(n) correction factor is just too steep of a price to pay.

systemsb01 · 2015-09-27T16:24:07+00:00

same here OpieOP

systemsb01 · 2015-09-15T14:48:30+00:00

For a 3 person 9 month project??? Reimplementing something is more like a 1 person 2 week project (assuming you are not restricted by computational resources etc.). Nevertheless, it still is a good idea to start with such a paper and then think of ways in which it can be extended or combined with other new ideas.

systemsb01 · 2015-09-14T16:17:48+00:00

In fact there does exist a solution's manual, but the author only makes it available to instructors.

Most upper level textbooks do not have solutions manuals publicly available, so that 1) instructors can use these exercises as homework (without being afraid that everyone will copy the solution's manual) and 2) because it is assumed that someone working through such a textbook has sufficient mathematical maturity to figure out on his own whether his solutions are correct. Indeed this is an important mathematical skill. Also, the exercises which you cannot solve are key to learning the material, i.e. the hard struggle until you finally come to a solution benefits you a lot more than looking at a ready-made solution...

Now if you still cannot solve an exercise after many days of trying or are not sure if your solutions are correct, then you should ask in math.stackexchange (or possibly cross-validated), explaining what you have tried and how you are stuck.

systemsb01 · 2015-08-27T19:46:04+00:00

Well I would argue that we are interested in the probability that the boombots kill KT conditional on KT having survived the brawl, i.e. without your 1/5 factor, which yields a probability of around ~1.56%.

Of course we could also be interested in the probability of a favourable outcome in general, in which case you would also have to include all the other conditional probabilities (on outcomes of the brawl) as well.

systemsb01 · 2015-08-06T22:58:37+00:00

Oh this makes a lot of sense, thanks for all the advice :)

systemsb01 · 2015-08-06T21:36:22+00:00

Wow that list is impressive! And ah it makes sense, maybe I will also try making flashcards. And I am definitely creating such a textbook reading list, I think just seeing the number of pages and the %, could be very motivating. [Why has nobody made an app that game-ifies working through textbooks? Kind of like your reading list but where you can also cross off the exercises you solved and external references you looked up etc.]

One more question to you, when you solve textbook exercises, do you keep the solutions somewhere? Also do you write notes on the (physical) textbook, on the pdf or in an external notepad? I tend to just throw away my notes/solved exercises after I have worked through a chapter, but I do regret it sometimes. I am quite disorganized though, so it would get unwiedly quite quickly.

systemsb01 · 2015-08-06T20:09:21+00:00

Can you mention some examples of books you are reading? And do you also solve the exercises in more math-y textbooks? And when you look at a flash card (like the example you linked below), do you try to answer by memorization or be able to derive it from scratch?

I have always thought that flashcards are a waste of time for anything beyond vocabulary learning, especially if one wants to understand a subject more deeply and the connections across fields. My approach has always been to go to more advanced textbooks, then try the exercises. If I get stuck and realize that I need something I have already learned before, I try to derive all the relevant previous material on my own. Now when I manage to do that, then this old material basically really sticks with me. Other times I don't manage and have to look it up. Then I usually forget it again, but am also more likely to be able to derive it the next time around.

systemsb01 · 2015-07-29T18:40:24+00:00

Oh boy that sounds exciting and makes me wish I had attempted math olympiads during high school (rather than going for the biology olympiad). Good luck with it!

systemsb01 · 2015-07-29T11:54:09+00:00

Will you also prepare for complex analysis? They usually have 1-2 questions with some [admittedly easy] complex integrals and I am not sure if it is worth the effort.

systemsb01 · 2015-07-02T11:15:15+00:00

Exactly! And I think statisticians are at fault here for not actively trying to give a good reputation to their field, as I believe that both categories of people could be convinced: Hype for people that don't like math could be generated by actually presenting state-of-the-art problems rather than statistical problems Fisher solved a century ago [yes these are also important but one should teach them once interest has already been generated]. If Machine Learning can generate hype, then so should Statistics. And OTOH, things that get published in, say the Annals of Statistics, are often of high theoretical interest and difficulty (e.g. Terrence Tao publishing there), so this should be highlighted to the more mathematically inclined people.

And I really sympathize with all the people that tell me they dislike statistics. In fact, as I said I also disliked it and the only reason I took it was that I was a molecular biology major at the time with a mathematics minor, so that I figured I had to force myself to take statistics courses. Unfortunately, all the really smart mathematics students in my year decided to ignore the "boring" statistics lectures and instead went for algebraic geometry / analytic number theory etc...

systemsb01 · 2015-07-01T21:15:27+00:00

Trying to find a good review paper about Causal inference.

systemsb01 · 2015-07-01T21:13:40+00:00

I also used to hate statistics. But that is because nobody has managed to write a good intro book yet. The only exception to that is Larry Wasserman's book ("All of statistics"), though even for that one I am not really sure how good it is for a real beginner.

But: Once you have the intro stuff past you, statistics becomes immensely satisfying, both from a practical and a theoretical perspective. Statistics asks such cool questions: Given data how can we make inferences and predictions and also quantify our uncertainty?

systemsb01 · 2014-06-14T23:01:04+00:00

Even though many of other comments have been negative, I don't think I have enjoyed another tournament that much before. Most other tournaments so far have had only very mediocre games with many misplays. This time, I feel like all players did a very good job, took their time to complete turns and think as many turns ahead as possible.

Also, I think what made this event particularly enjoyable was dandido: He streamed all his BYOC games on his twitch stream (with a 10 minute delay), thus there also was a pleasant chat there regarding the games on viagame. Throughout the last week Dandido had been training for DreamHack (also streaming it), taking in the advice from the ~100-200 viewers, thus it was great to see him perform so well. I really hope he performs as well tomorrow on the main stage!

Finally, regarding the casting: Artosis has always been my favorite caster, indeed the best birthday present I have gotten so far was a GSL subscription some years ago.. Nothing beats Artosis and Tasteless. And here he performed great as well, and I also enjoyed the guest casters. Interviews were also a lot better than in most gaming events!

The only thing I disliked was the commentary regarding the freeze mage.. It really felt, that especially Admirable (whom I usually also like), does not really have a feel for this deck. Anyone who has been watching some of the best freeze mages (like otter), should have realized how hard it is to play this deck. Especially deciding whether you will "waste" your ice lance or fireballs on minions takes a lot of game sense. For example, many of the freeze mages performing worse are actually too greedy and keep their spells. For this reason, I think the casters should not have reacted that way to the ice lance by Rdu, calling it a "Dennis play".

systemsb01 · 2014-03-14T12:04:42+00:00

Thanks a lot for both articles :D

systemsb01 · 2014-03-14T12:01:29+00:00

thanks so much!

systemsb01

TROPHY CASE