11 ms·

Statistical Computing for Scientists and Engineers

186 points by mubaris 9y ago

Myrmornis 9y ago
This lecture course appears to attempt to cover way, way too much material in each lecture.
- jofer 9y ago
  It looks like a graduate class. It's not expected that this is the first time someone in the course has come across most of the concepts and methods. The syllabus could almost be from an inverse theory class in my field (geology), albeit one with more focus on the underlying mathematics. I don't think it's trying to do too much, it's just not trying to be a intro course.
  - digitalzombie 9y ago
    > it's just not trying to be a intro course. > It's not expected that this is the first time someone in the course has come across most of the concepts and methods. It is just cramming at least 4-5 stats classes in there that's all. Unless you get those right combination it'll be your first time anyway. There are multivariate stat, 2 courses of Bayesian ( 1 tradition and 1 nonparametric), 1 course of comp stat, and whatever the heck else I've haven't encounter. Bayesian isn't even taught in my grad program at all. I had 2 months to learn Bayesian. I disagree with the not expected to be first time and maaaaybe it's just to get your feet wet. Considering the stuff the professor is going over it seem more sink or swim.
    - ted_dunning 9y ago
      Bayesian statistics isn't even taught in your grad program?!?! That seems impossibly out of step with the world today. How can they explain something as simple as a multi-armed bandit from that point of view?
- apohn 9y ago
  The professor himself probably agrees with you. In lecture 18 the professor states At 3:40 - For this topic he actually has has 6 or 7 lectures, 100 pages of notes. But it's compressed to an introduction. At 7:10 - He states HMM is 2-3 lectures, but he's going to compress it to half a lecture. Honestly, I had courses like this in grad school. The were typically seminars and usually graded on a curve because people crammed stuff into their brains as quickly as possible and barely understood anything! They were meant to give you a broad coverage of a field, not comprehensively cover any particular set of topics.
  - j9461701 9y ago
    Cryptography in undergrad was a course like that at my school. Here is the most basic understanding of group theory we can possibly get away with, here is how it applies to RSA. Next lecture now we're moving on to elliptical curves.... Perhaps some topics are simply too big to really teach effectively through lectures. You need to go digging on your own to understand all the myriad details, and put in the hours alone with a textbook.
    - adrianN 9y ago
      The point of those lectures, especially in undergrad, is to reduce the "unknown unknowns". You don't know that you could look up how some technique actually works if you haven't ever heard about it. If you want all the details you need good textbooks, or, for more advanced topics, the original papers.
  - severine 9y ago
    How much material have the profs digested in order to make this huge curriculums? Because they're not just listing "anything they've read about the subject", are they?
    - apohn 9y ago
      Some professors have had 20+ years to digest an enormous amount of information. Keep in mind they have 2 major advantages over people in industry when learning this stuff. At universities that offer PhDs, being a professor forces you to develop a strong theoretical foundation, which makes it easier to grasp the theory of related subjects. Example: If you have a deep understanding of math/statistics theory, it's much easier to understand a paper on machine learning, even if you're not a computer science professor. Professors at research oriented universities supervise PhD research students and each student tends to be a multiplier on their supervisor's knowledge since each PhD is a collection and extension of an existing research area. A good PhD student is basically a massive funnel of information. I'll also say, don't ascribe more abilities or skills to a professor than what you see in the presentation. Sometimes having a strong theoretical orientation/experience comes at the expense of a practical one. Don't assume that having a strong theoretical foundation in Stats/ML automatically makes you a good Data Scientist or ML Engineer.
MichailP 9y ago
I can never shake off the feeling that statistics is somewhat lacking compared to the rest of "fundamental" sciences. To me it just lacks sort of brutal honesty that is present say in physics. And how come that the math often "looks" scary? To me this also seems intentional, like someone is trying to hide the lack of real content. Honestly, do we really need a whole field to run a curve through a cloud of points? Please, dispute me in comments below, I would really like to be wrong about this. Edit: Let the down-voting begin
- eastWestMath 9y ago
  It’s been a while since I’ve done serious statistical work (I’m a logician. There’s many different kinds of distributions (i.e. lines through a field), from which you would make very different inferences - relationships which satisfy a power law can of look like a Gaussian/normal distribution. Finding the “right” distribution when you know you’re working with incomplete data isn’t simple. I can assure you they aren’t trying to make the math look scary - to be a bit blunt, a comment like that’s usually a sign that someone hasn’t made a serious attempt to engage with a field. That sort of bad faith regarding a core discipline of the mathematical sciences probably should be downvoted.
- closed 9y ago
  I have seen people in physics and other physical sciences work with statistics. My sense is that the less someone has to worry about "measurement error", the less intuitive statics seems to them. For example, in some areas of research, once you have the right instrument take measurements, you can just plot the results, and your measurements (or some transformation of them) will show up as a linear function of whatever you manipulated. But really, I'm not totally sure what you mean, since in any situation where there's uncertainty, it makes sense to me that you'd want to try to capture that uncertainty in your analysis, and that's statistics. Make those analyses more complex (e.g. taking measurements from a field with obvious spatial dependence between measures), and the models become more complex too.
  - geomark 9y ago
    That's really the issue, isn't it? That nearly all data is at least somewhat noisy. Making inferences from it is just a big blur without a systematic way to handle it.
melling 9y ago
I’ve got this one and a few others on a Github repo: https://github.com/melling/MathAndScienceNotes/tree/master/statistics https://github.com/melling/MathAndScienceNotes/tree/master/s... I haven’t listened to the Notre Dame course yet. How would others rate it? I have gone through the UC Berkeley Irvine 131a class. I have high-level notes: https://github.com/melling/MathAndScienceNotes/blob/master/statistics/uc_irvine_131a/2013_stats_131A_uc_irvine.md https://github.com/melling/MathAndScienceNotes/blob/master/s... and detailed notes in PDF for the first four classes; https://github.com/melling/MathAndScienceNotes/blob/master/statistics/uc_irvine_131a/stats_131a_lecture_01.pdf https://github.com/melling/MathAndScienceNotes/blob/master/s... Actually, wrote this up in a blog yesterday: https://h4labs.wordpress.com/2017/12/30/learning-probability-and-statistics/ https://h4labs.wordpress.com/2017/12/30/learning-probability...
- koprulusector 9y ago
  Thank you so much for sharing this!!!

186 points by mubaris 9y ago

Myrmornis 9y ago

This lecture course appears to attempt to cover way, way too much material in each lecture.

jofer 9y ago
It looks like a graduate class. It's not expected that this is the first time someone in the course has come across most of the concepts and methods. The syllabus could almost be from an inverse theory class in my field (geology), albeit one with more focus on the underlying mathematics. I don't think it's trying to do too much, it's just not trying to be a intro course.
- digitalzombie 9y ago
  > it's just not trying to be a intro course. > It's not expected that this is the first time someone in the course has come across most of the concepts and methods. It is just cramming at least 4-5 stats classes in there that's all. Unless you get those right combination it'll be your first time anyway. There are multivariate stat, 2 courses of Bayesian ( 1 tradition and 1 nonparametric), 1 course of comp stat, and whatever the heck else I've haven't encounter. Bayesian isn't even taught in my grad program at all. I had 2 months to learn Bayesian. I disagree with the not expected to be first time and maaaaybe it's just to get your feet wet. Considering the stuff the professor is going over it seem more sink or swim.
  - ted_dunning 9y ago
    Bayesian statistics isn't even taught in your grad program?!?! That seems impossibly out of step with the world today. How can they explain something as simple as a multi-armed bandit from that point of view?
apohn 9y ago
The professor himself probably agrees with you. In lecture 18 the professor states At 3:40 - For this topic he actually has has 6 or 7 lectures, 100 pages of notes. But it's compressed to an introduction. At 7:10 - He states HMM is 2-3 lectures, but he's going to compress it to half a lecture. Honestly, I had courses like this in grad school. The were typically seminars and usually graded on a curve because people crammed stuff into their brains as quickly as possible and barely understood anything! They were meant to give you a broad coverage of a field, not comprehensively cover any particular set of topics.
- j9461701 9y ago
  Cryptography in undergrad was a course like that at my school. Here is the most basic understanding of group theory we can possibly get away with, here is how it applies to RSA. Next lecture now we're moving on to elliptical curves.... Perhaps some topics are simply too big to really teach effectively through lectures. You need to go digging on your own to understand all the myriad details, and put in the hours alone with a textbook.
  - adrianN 9y ago
    The point of those lectures, especially in undergrad, is to reduce the "unknown unknowns". You don't know that you could look up how some technique actually works if you haven't ever heard about it. If you want all the details you need good textbooks, or, for more advanced topics, the original papers.
- severine 9y ago
  How much material have the profs digested in order to make this huge curriculums? Because they're not just listing "anything they've read about the subject", are they?
  - apohn 9y ago
    Some professors have had 20+ years to digest an enormous amount of information. Keep in mind they have 2 major advantages over people in industry when learning this stuff. At universities that offer PhDs, being a professor forces you to develop a strong theoretical foundation, which makes it easier to grasp the theory of related subjects. Example: If you have a deep understanding of math/statistics theory, it's much easier to understand a paper on machine learning, even if you're not a computer science professor. Professors at research oriented universities supervise PhD research students and each student tends to be a multiplier on their supervisor's knowledge since each PhD is a collection and extension of an existing research area. A good PhD student is basically a massive funnel of information. I'll also say, don't ascribe more abilities or skills to a professor than what you see in the presentation. Sometimes having a strong theoretical orientation/experience comes at the expense of a practical one. Don't assume that having a strong theoretical foundation in Stats/ML automatically makes you a good Data Scientist or ML Engineer.

MichailP 9y ago

I can never shake off the feeling that statistics is somewhat lacking compared to the rest of "fundamental" sciences. To me it just lacks sort of brutal honesty that is present say in physics. And how come that the math often "looks" scary? To me this also seems intentional, like someone is trying to hide the lack of real content. Honestly, do we really need a whole field to run a curve through a cloud of points? Please, dispute me in comments below, I would really like to be wrong about this. Edit: Let the down-voting begin

eastWestMath 9y ago
It’s been a while since I’ve done serious statistical work (I’m a logician. There’s many different kinds of distributions (i.e. lines through a field), from which you would make very different inferences - relationships which satisfy a power law can of look like a Gaussian/normal distribution. Finding the “right” distribution when you know you’re working with incomplete data isn’t simple. I can assure you they aren’t trying to make the math look scary - to be a bit blunt, a comment like that’s usually a sign that someone hasn’t made a serious attempt to engage with a field. That sort of bad faith regarding a core discipline of the mathematical sciences probably should be downvoted.
closed 9y ago
I have seen people in physics and other physical sciences work with statistics. My sense is that the less someone has to worry about "measurement error", the less intuitive statics seems to them. For example, in some areas of research, once you have the right instrument take measurements, you can just plot the results, and your measurements (or some transformation of them) will show up as a linear function of whatever you manipulated. But really, I'm not totally sure what you mean, since in any situation where there's uncertainty, it makes sense to me that you'd want to try to capture that uncertainty in your analysis, and that's statistics. Make those analyses more complex (e.g. taking measurements from a field with obvious spatial dependence between measures), and the models become more complex too.
- geomark 9y ago
  That's really the issue, isn't it? That nearly all data is at least somewhat noisy. Making inferences from it is just a big blur without a systematic way to handle it.

melling 9y ago

I’ve got this one and a few others on a Github repo: https://github.com/melling/MathAndScienceNotes/tree/master/statistics https://github.com/melling/MathAndScienceNotes/tree/master/s... I haven’t listened to the Notre Dame course yet. How would others rate it? I have gone through the UC Berkeley Irvine 131a class. I have high-level notes: https://github.com/melling/MathAndScienceNotes/blob/master/statistics/uc_irvine_131a/2013_stats_131A_uc_irvine.md https://github.com/melling/MathAndScienceNotes/blob/master/s... and detailed notes in PDF for the first four classes; https://github.com/melling/MathAndScienceNotes/blob/master/statistics/uc_irvine_131a/stats_131a_lecture_01.pdf https://github.com/melling/MathAndScienceNotes/blob/master/s... Actually, wrote this up in a blog yesterday: https://h4labs.wordpress.com/2017/12/30/learning-probability-and-statistics/ https://h4labs.wordpress.com/2017/12/30/learning-probability...

koprulusector 9y ago
Thank you so much for sharing this!!!