What is machine learning?

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Essentially, it is a method of teaching computers to make and improve predictions or behaviors based on some data. What is this “data”? Well, that depends entirely on the problem. It could be readings from a robot’s sensors as it learns to walk or the correct output of a program for certain input.

Machine learning is a field of computer science, probability theory, and optimization theory that allows complex tasks to be solved for which a logical/procedural approach would not be possible or feasible.

Another way to think about machine learning is that it is “pattern recognition” – the act of teaching a program to react to or recognize patterns.

There are several different categories of machine learning, including (but not limited to):

  • Supervised learning
  • Reinforcement learning

Supervised Learning
In supervised learning, you have some really complex function (mapping) from inputs to outputs, you have lots of examples of input/output pairs, but you don’t know what that complicated function is. A supervised learning algorithm makes it possible, given a large data set of input/output pairs, to predict the output value for some new input value that you may not have seen before. The basic method is that you break the data set down into a training set and a test set. You have some model with an associated error function that you try to minimize over the training set, and then you make sure that your solution works on the test set.

Once you have repeated this with different machine learning algorithms and/or parameters until the model performs reasonably well on the test set, then you can attempt to use the result on new inputs. Note that in this case, the program does not change, only the model (data) is changed. Although one could, theoretically, output a different program, that is not done in practice, as far as I am aware. An example of supervised learning would be the digit recognition system used by the post office, where it maps the pixels to labels in the set 0…9, using a large set of pictures of digits that were labeled by hand as being in 0…9.

Reinforcement Learning
In reinforcement learning, the program is responsible for making decisions, and it periodically receives some sort of award/utility for its actions. However, unlike in the supervised learning case, the results are not immediate; the algorithm could prescribe a large sequence of actions and only receive feedback at the very end. In reinforcement learning, the goal is to build up a good model such that the algorithm will generate the sequence of decisions that lead to the highest long-term utility/reward.

A good example of reinforcement learning is teaching a robot how to navigate by giving a negative penalty whenever its bump sensor detects that it has bumped into an object. If coded correctly, it is possible for the robot to eventually correlate its range finder sensor data with its bumper sensor data and the directions that send to the wheels, and ultimately choose a form of navigation that results in it not bumping into objects.

What does machine learning code do ?

Depends on the type of machine learning you’re talking about. Machine learning is a huge field, with hundreds of different algorithms for solving myriad different problems.

When we say machine learns, does it modify the code of itself or it modifies history (Data Base) which will contain the experience of code for given set of inputs ?

One example of code actually being modified is Genetic Programming, where you essentially evolve a program to complete a task (of course, the program doesn’t modify itself – but it does modify another computer program).

Neural networks, on the other hand, modify their parameters automatically in response to prepared stimuli and expected responses. This allows them to produce many behaviors (theoretically, they can produce any behavior because they can approximate any function to arbitrary precision, given enough time).

 should note that your use of the term “database” implies that machine learning algorithms work by “remembering” information, events, or experiences. This is not necessarily (or even often!) the case.

Neural networks, which I already mentioned, only keep the current “state” of the approximation, which is updated as learning occurs. Rather than remembering what happened and how to react to it, neural networks build a sort of “model” of their “world.” The model tells them how to react to certain inputs, even if the inputs are something that it has never seen before.

This last ability – the ability to react to inputs that have never been seen before – is one of the core tenets of many machine learning algorithms. Imagine trying to teach a computer driver to navigate highways in traffic. Using your “database” metaphor, you would have to teach the computer exactly what to do in millions of possible situations. An effective machine learning algorithm would (hopefully!) be able to learn similarities between different states and react to them similarly.

The similarities between states can be anything – even things we might think of as “mundane” can really trip up a computer! For example, let’s say that the computer driver learned that when a car in front of it slowed down, it had to slow down too.

For a human, replacing the car with a motorcycle doesn’t change anything – we recognize that the motorcycle is also a vehicle. For a machine learning algorithm, this can actually be surprisingly difficult! A database would have to store information separately about the case where a car is in front and where a motorcycle is in front. A machine learning algorithm, on the other hand, would “learn” from the car example and be able to generalize to the motorcycle example automatically.

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