Quote:
Originally Posted by vikkamath
Ok. Great Question. Let's look at this from a point of view of what we already know. i.e the three points in the 'essence of machine learning'.
Problem : Optimal Cycle for Traffic Lights
1. A pattern exists: If you think about it intuitively, there should be some correlation (speaking loosely) between the traffic at a light on the intersection with the amount of time that a car spends in that light. Right? If there are a lot of cars, you'd expect that the light is green for a far greater duration than a light in which there are only one or two cars.
2. We cannot pin in down mathematically: You could, with a great amount of difficulty try and pin in down mathematically, but heck, that's what learning's for right? You make the machine do the dirty work and you collect the check at the end of the day.
3. We have data on it:
It's quite evident that collecting data for this kind of problem isn't too much of a big deal. You could put a bunch of cameras and have some grad students or interns write some computer vision program to see how many cars there are at a light and measure the duration of time that that light is green and so on (this is just one example of how you might go about collecting data  it's eventually up to you).
Problem: Calculating how much time a body takes to fall to the ground
1. A pattern exists :
Newtons laws of motions and all that have showed us that a pattern does indeed exist and that..
2. It can be described mathematically:
Since there is a closed form solution to your problem, wouldn't you rather use that? Learning from data inherently has, associated with it, a generalization error. There are so many things that could possibly go wrong  you might choose the wrong hypothesis set (you might think that the relationship between the time of flight and the height of the object from the ground is linear when it is actually quadratic). Do you see what I am saying? Why would you want to settle for an approximate solution when an exact solution is well within your reach?

Thanks for the reply. However I am still not clear on these points
1. I do agree that there is a pattern that exists between traffic flow to an intersection and the amount of time the green light should be on. But when you are doing machine learning you need data points which go like
(traffic flow1, time1), (traffic flow2, time2) and so on.
If this is the training data, then obviously somebody arrived at these values. Because the time is set by humans and because we are presenting these as a training examples, it means that we already know the formula for computing the correct time.
2. If we don't have the exact formula for computing the traffic light times, then we might go about collecting the data by experimenting. I know that technologically it is not difficult to do, but what about the cost of experimenting with traffic lights on busy intersections. Is that something we want to do.
3. The time it takes for an object to fall to ground can be modeled exactly only if the object is falling in vacuum. If its falling through atmosphere, then its no longer certain, because of density of atmosphere, wind conditions so on. But physics is not my area and I don't want to make any strong claims on this.
4. I think its easier to drop objects and record times than it is to conduct experiments at busy traffic junctions and drive people crazy
Anyway, I don't want to go on and on on this topic. I do feel that this question is quite subjective.
thanks
Arun