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I'm seriously considering quitting my job and studying ML for a few months in a desperate attempt to get work in projects like this. I feel like I'm missing out

by sown 14y ago

I'm seriously considering quitting my job and studying ML for a few months in a desperate attempt to get work in projects like this. I feel like I'm missing out but too dumb for traditional grad school.

p1esk 14y ago
That's what I did a few months ago - quit my job and decided to go to a grad school to study AI (with focus on neural nets and ML).
- ralfd 14y ago
  What did you learn so far about neural nets? I recently looked into machine learning and naively thought I could find at least one practical fun tutorial "Here is a neural network API in C, you have to do that and this to let a simulated robot evade obstacles or learn to play Asteroids". Instead my (extremely superficial) search did find that neural nets are pretty arcane, genetic algorithms trapped in local minima and you are faster and better of coding logic yourself, developing a mathematical model to calculate results, instead searching for patterns in vast sets of data.
  - mikedmiked 14y ago
    try python's scikit.learn library
  - ippisl 14y ago
    Here's a fun toy project using neural networks. http://slowping.com/2012/self-driving-lego-mindstorms-robot/ http://slowping.com/2012/self-driving-lego-mindstorms-robot/
  - Dn_Ab 14y ago
    If you are trying to make game AI then neural networks are a bad idea. But if you are just trying to have some fun learning then do whatever you want. That said, MLPs are hard to use properly, not a good place to start. A good way to start "AI". Write a decision tree they will serve you well and with boosting do even better. Basic but useful stuff: logistic regression, armed bandits, weighted experts, kNearest, k means , Kernel Density estimation and Naive Bayes. That covers online, ensemble, super and unsuper vised algorithms. Goodluck!
  - caster_cp 14y ago
    SVMs are a relatively easy to use (but not to understand) method that yield impressive results for beginners. See the libsvm website, there are plenty of good material there (http://www.csie.ntu.edu.tw/~cjlin/libsvm/ http://www.csie.ntu.edu.tw/~cjlin/libsvm/). But as stated somewhere else here, game AI is a whole different story.
  - p1esk 14y ago
    I learned that neural nets research requires serious math knowledge, so that's what I'm working on currently. One fun project to start learning about NNs is a balancing pole problem. There are solutions in various languages. Yes, for some problems it might be faster and better to code logic yourself, but there are also tasks (such as pattern recognition) where NNs might be more effective.
- sown 14y ago
  I'm too dumb for gradschool. It'll have to be self taught.
Devilboy 14y ago
Have you seen the online courses? https://www.coursera.org/course/ml https://www.coursera.org/course/ml (From one of the authors of this paper!) https://www.coursera.org/course/vision https://www.coursera.org/course/vision https://www.coursera.org/course/computervision https://www.coursera.org/course/computervision Prof. Hinton's videos are very watchable: http://www.youtube.com/watch?v=AyzOUbkUf3M http://www.youtube.com/watch?v=AyzOUbkUf3M http://www.youtube.com/watch?v=VdIURAu1-aU http://www.youtube.com/watch?v=VdIURAu1-aU
- magoghm 14y ago
  If you like math, Caltech's "Learning from Data" is awesome http://work.caltech.edu/telecourse.html http://work.caltech.edu/telecourse.html
- sown 14y ago
  Yes, often. Thanks for the links.
roel_v 14y ago
"and studying ML for a few months" Could anyone with expertise say if this would be enough to build a foundation? How much math background do you need?
- jules 14y ago
  You need at least probability/statistics, linear algebra, calculus, and numerical methods. Once you know this machine learning is a relatively thin layer built on this math foundation. The real problem is learning the math foundation.
- jackpirate 14y ago
  It might be enough to be able to use tools that other people have built, and have a rough idea of what's going on under the hood, and how to select which algorithm to use in a very general sense. It won't be enough for you to be designing your own algorithms.

by sown 14y ago

p1esk 14y ago

That's what I did a few months ago - quit my job and decided to go to a grad school to study AI (with focus on neural nets and ML).

ralfd 14y ago
What did you learn so far about neural nets? I recently looked into machine learning and naively thought I could find at least one practical fun tutorial "Here is a neural network API in C, you have to do that and this to let a simulated robot evade obstacles or learn to play Asteroids". Instead my (extremely superficial) search did find that neural nets are pretty arcane, genetic algorithms trapped in local minima and you are faster and better of coding logic yourself, developing a mathematical model to calculate results, instead searching for patterns in vast sets of data.
- mikedmiked 14y ago
  try python's scikit.learn library
- ippisl 14y ago
  Here's a fun toy project using neural networks. http://slowping.com/2012/self-driving-lego-mindstorms-robot/ http://slowping.com/2012/self-driving-lego-mindstorms-robot/
- Dn_Ab 14y ago
  If you are trying to make game AI then neural networks are a bad idea. But if you are just trying to have some fun learning then do whatever you want. That said, MLPs are hard to use properly, not a good place to start. A good way to start "AI". Write a decision tree they will serve you well and with boosting do even better. Basic but useful stuff: logistic regression, armed bandits, weighted experts, kNearest, k means , Kernel Density estimation and Naive Bayes. That covers online, ensemble, super and unsuper vised algorithms. Goodluck!
- caster_cp 14y ago
  SVMs are a relatively easy to use (but not to understand) method that yield impressive results for beginners. See the libsvm website, there are plenty of good material there (http://www.csie.ntu.edu.tw/~cjlin/libsvm/ http://www.csie.ntu.edu.tw/~cjlin/libsvm/). But as stated somewhere else here, game AI is a whole different story.
- p1esk 14y ago
  I learned that neural nets research requires serious math knowledge, so that's what I'm working on currently. One fun project to start learning about NNs is a balancing pole problem. There are solutions in various languages. Yes, for some problems it might be faster and better to code logic yourself, but there are also tasks (such as pattern recognition) where NNs might be more effective.
sown 14y ago
I'm too dumb for gradschool. It'll have to be self taught.

Devilboy 14y ago

Have you seen the online courses? https://www.coursera.org/course/ml https://www.coursera.org/course/ml (From one of the authors of this paper!) https://www.coursera.org/course/vision https://www.coursera.org/course/vision https://www.coursera.org/course/computervision https://www.coursera.org/course/computervision Prof. Hinton's videos are very watchable: http://www.youtube.com/watch?v=AyzOUbkUf3M http://www.youtube.com/watch?v=AyzOUbkUf3M http://www.youtube.com/watch?v=VdIURAu1-aU http://www.youtube.com/watch?v=VdIURAu1-aU

magoghm 14y ago
If you like math, Caltech's "Learning from Data" is awesome http://work.caltech.edu/telecourse.html http://work.caltech.edu/telecourse.html
sown 14y ago
Yes, often. Thanks for the links.

roel_v 14y ago

"and studying ML for a few months" Could anyone with expertise say if this would be enough to build a foundation? How much math background do you need?

jules 14y ago
You need at least probability/statistics, linear algebra, calculus, and numerical methods. Once you know this machine learning is a relatively thin layer built on this math foundation. The real problem is learning the math foundation.
jackpirate 14y ago
It might be enough to be able to use tools that other people have built, and have a rough idea of what's going on under the hood, and how to select which algorithm to use in a very general sense. It won't be enough for you to be designing your own algorithms.