Well posed Machine Learning

Well posed Machine Learning


Well posed Machine Learning

Simply to say precisely it is nothing but  learning by experience.

An agent or a Person solves a problem or Task T, Performance P, gain some experience E.

By doing this task T with performance P this agent will give some experience is denoted by E.

If P is the person measured at T it can improve E, with the help of Performance it will increase the experience.


If the performance is good, he will try to improve more good and become best.

If the performance is not good in that case he will understand law pols he will try to improve the experience he had gain through that, used some experience he adds some more new methodology to improve more that is well posed learning problem.


Let us see in all these there scenario what is the task T, what is the Performance P and what is the experience E that the learner will get some particular task.

Here if you can see that here the problem

Task 1 : what the user support to do

Task 2: how the user Performance should be their what is the user experience were is going to out of that.



Task (T)

Performance (P )


Experience( E)

Playing Checkers Learning


Playing against opponents to win the game

If you are writing will be an exam,  your task is finish  all the questions in the given time in checkers need to win over the opponents


Make perfect moves in order to win the game

So that your Performance is good

If you are not making perfect moves so that your moves get wasted

Example: Candy Crush

It plays itself to improve


Handwriting Recognition Learning


Classifying the images and Text


Better Classification


A database of homework test that means it have to some inbuilt database so that observing the observation of the data from the database it will learn itself on it will perform


Robot Driving Learning Problem


Drive the car in a four lane highway


Source to destination the average distance travelled should be longer and safer


Image vehicle on road


Face Recognition Problem


Redirecting different types of data


Measurable to predict maximum type of faces


Training machine with maximum amount of dataset of different face images


To better filter emails as spam or not


Classifying emails as spam or not


Measures the fraction of email accordingly classified as a spam or not


Observing you label email as spam or not


Fruit prediction problem


Forecasting different fruits for recognition


Measurable to predict maximum variety of fruits


Balancing the machine with a large datasets of fruits image


Automatic translation of documents


Translating one type language used in a document to other languages


Measurable to convert one language to another efficiency


Training machine with a large amount of dataset of different types of languages