5 ms·

Keep in mind, that these videos don't teach you any machine learning. They teach you how to use programs, which perform machine learning for you.

Roritharr 11y ago
I first played Video Games, then modded them, then wrote my own. For me that's a natural evolution and I find it hard to accept that in certain fields you are supposed to get in contact with the academical background first before jumping in to actual usage. It's like having to understand the detailed physics of an internal combustion engine before driving or before repairing the car.
- thaw13579 11y ago
  I would agree that there's plenty of theory that isn't necessary to know, but anyone using a machine learning algorithm should at least understand what the mathematical machinery can and can't do. A big part of this this knowing how best to fit a model to the data, which usually requires knowledge of mathematical obscurities to avoid things like overfitting, local optima, etc. It's not glamorous stuff though, so it usually isn't brought up in presentations like this one.
  - wpietri 11y ago
    I agree people should know that eventually, but I'm suspicious that they really need to know it first, or even early on. My early coding years were spent typing in BASIC game programs from magazines (wumpus hunters represent!), tweaking them, and later making up my own. There was a lot of theory that I could have benefited from, but I never had the motivation to learn it until later, when learning the theory solved problems I had actually experienced.
    - thaw13579 11y ago
      Good point, I was thinking more about people using ML in a professional capacity. It's interesting to think about how best to teach it, and it totally seems reasonable to save the math for later in some cases. Another interesting challenge is that ML fails in less obvious ways than coding in general, and maybe in some way, intuition for that is something to be taught early.
    - njwi332 11y ago
      The main difference, and reason why your analogy isn't entirely accurate, is that while with games you can see when things aren't working, when using ML or stats you will _always_ get a number. Whether or not that number is meaningful requires some amount of domain knowledge a lot of the time. I have a degree in stats and someone at work who does not was trying to use these frameworks to analyse log files. When I had a look at it, his results were showing that they were statistically significant, but the data didn't look anything like a linear relationship and fitting it to a regression wasn't a valid move. That's a simplistic example but even in the relatively simple realm of linear regression there are more difficult traps to spot, like heterostedasticity or error normality.
      - wpietri 11y ago
        I agree that one shouldn't apply ML in a commercial context without understanding it. But I think that's true about almost anything. I can't think of a technology I use for which I don't have a corresponding "novices did it all wrong" story. But here we're talking about a series of intro videos and the appropriate pedagogical approach. It really could be that ML has more subtle failure modes than programming, although I'm suspicious; I remember a lot of my novice C issues where the program did happen to appear to work, at least for short periods, even though my code was terrible. But if it is, I think the trick there isn't to prescribe a heavier dose of theory, it's to get people to experience problems like you describe in a way where they can quickly detect and learn from them.
splike 11y ago
The mathematics of the model you end up using at the end of the day is such a small part of the overall picture when making use of machine learning.
- machinelearning 11y ago
  This is so false. Applying machine learning to a real world problem requires correct intuition and the ability to quantify tradeoffs mathematically. This is developed by understanding the math behind the model and what the tradeoffs are.
  - splike 11y ago
    I completely disagree. Yes, we need to quantify tradeoffs between models mathematically, but that does not not require knowledge of the mathematics behind the models themselves. With cross validation, I can estimate the effectiveness of many black box models, without looking inside them. This step is called error estimation, and comes before model selection. I can arrive at a pretty good model by a combination of correct methodology and brute force. It is this methodology that makes up much more of the overall picture. You could give me a black box, a rough range of parameters it takes, and I can tell you how likely it is to work well. This approach doesn't scale well to bigger problems, but I doubt tackling Big Data problems is the intention behind this course.
    - bartkappenburg 11y ago
      And I disagree with that. Tuning parameters and selecting features needs (a) understanding of the model(s) used and (b) an understanding of the data. 'Brute forcing' these steps can grow exponentially in time (eg. feature selection out of n features takes 2^n combinations) and makes your approach not only very inefficient but also doesn't predict if you have a good model. Your approach makes sensitivity analysis makes very very hard.
      - pitchka 11y ago
        And yet again these stuff can be automated with contextual bandits or gaussian processes. Instead of bruteforcing one can just put yet another system to find the parameters for them.
Joeri 11y ago
To actually learn machine learning algorithms i found the coursera (stanford) course to be excellent. Minimal programming and math knowledge needed. https://www.coursera.org/learn/machine-learning/home/info https://www.coursera.org/learn/machine-learning/home/info
- josso 11y ago
  I also recommend Welch Labs’ Neural Network Demystified-series[0]. It is a combination of the Coursera course and the YouTube videos. It get’s into some of the math, while still keeping it basic. [0]: https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&index=1 https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX...
kowdermeister 11y ago
That's good enough. You don't need to understand each filter in Photoshop to achieve your desired result.

Roritharr 11y ago

I first played Video Games, then modded them, then wrote my own. For me that's a natural evolution and I find it hard to accept that in certain fields you are supposed to get in contact with the academical background first before jumping in to actual usage. It's like having to understand the detailed physics of an internal combustion engine before driving or before repairing the car.

thaw13579 11y ago
I would agree that there's plenty of theory that isn't necessary to know, but anyone using a machine learning algorithm should at least understand what the mathematical machinery can and can't do. A big part of this this knowing how best to fit a model to the data, which usually requires knowledge of mathematical obscurities to avoid things like overfitting, local optima, etc. It's not glamorous stuff though, so it usually isn't brought up in presentations like this one.
- wpietri 11y ago
  I agree people should know that eventually, but I'm suspicious that they really need to know it first, or even early on. My early coding years were spent typing in BASIC game programs from magazines (wumpus hunters represent!), tweaking them, and later making up my own. There was a lot of theory that I could have benefited from, but I never had the motivation to learn it until later, when learning the theory solved problems I had actually experienced.
  - thaw13579 11y ago
    Good point, I was thinking more about people using ML in a professional capacity. It's interesting to think about how best to teach it, and it totally seems reasonable to save the math for later in some cases. Another interesting challenge is that ML fails in less obvious ways than coding in general, and maybe in some way, intuition for that is something to be taught early.
  - njwi332 11y ago
    The main difference, and reason why your analogy isn't entirely accurate, is that while with games you can see when things aren't working, when using ML or stats you will _always_ get a number. Whether or not that number is meaningful requires some amount of domain knowledge a lot of the time. I have a degree in stats and someone at work who does not was trying to use these frameworks to analyse log files. When I had a look at it, his results were showing that they were statistically significant, but the data didn't look anything like a linear relationship and fitting it to a regression wasn't a valid move. That's a simplistic example but even in the relatively simple realm of linear regression there are more difficult traps to spot, like heterostedasticity or error normality.
    - wpietri 11y ago
      I agree that one shouldn't apply ML in a commercial context without understanding it. But I think that's true about almost anything. I can't think of a technology I use for which I don't have a corresponding "novices did it all wrong" story. But here we're talking about a series of intro videos and the appropriate pedagogical approach. It really could be that ML has more subtle failure modes than programming, although I'm suspicious; I remember a lot of my novice C issues where the program did happen to appear to work, at least for short periods, even though my code was terrible. But if it is, I think the trick there isn't to prescribe a heavier dose of theory, it's to get people to experience problems like you describe in a way where they can quickly detect and learn from them.

splike 11y ago

The mathematics of the model you end up using at the end of the day is such a small part of the overall picture when making use of machine learning.

machinelearning 11y ago
This is so false. Applying machine learning to a real world problem requires correct intuition and the ability to quantify tradeoffs mathematically. This is developed by understanding the math behind the model and what the tradeoffs are.
- splike 11y ago
  I completely disagree. Yes, we need to quantify tradeoffs between models mathematically, but that does not not require knowledge of the mathematics behind the models themselves. With cross validation, I can estimate the effectiveness of many black box models, without looking inside them. This step is called error estimation, and comes before model selection. I can arrive at a pretty good model by a combination of correct methodology and brute force. It is this methodology that makes up much more of the overall picture. You could give me a black box, a rough range of parameters it takes, and I can tell you how likely it is to work well. This approach doesn't scale well to bigger problems, but I doubt tackling Big Data problems is the intention behind this course.
  - bartkappenburg 11y ago
    And I disagree with that. Tuning parameters and selecting features needs (a) understanding of the model(s) used and (b) an understanding of the data. 'Brute forcing' these steps can grow exponentially in time (eg. feature selection out of n features takes 2^n combinations) and makes your approach not only very inefficient but also doesn't predict if you have a good model. Your approach makes sensitivity analysis makes very very hard.
    - pitchka 11y ago
      And yet again these stuff can be automated with contextual bandits or gaussian processes. Instead of bruteforcing one can just put yet another system to find the parameters for them.

Joeri 11y ago

To actually learn machine learning algorithms i found the coursera (stanford) course to be excellent. Minimal programming and math knowledge needed. https://www.coursera.org/learn/machine-learning/home/info https://www.coursera.org/learn/machine-learning/home/info

josso 11y ago
I also recommend Welch Labs’ Neural Network Demystified-series[0]. It is a combination of the Coursera course and the YouTube videos. It get’s into some of the math, while still keeping it basic. [0]: https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&index=1 https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX...

kowdermeister 11y ago

That's good enough. You don't need to understand each filter in Photoshop to achieve your desired result.