Learning

“Learning denotes changes in a system that enables system to do the same task more efficiently next time.”

Machine Learning:-Definition

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P,if its performance at tasks in T,as measured by P,improves with experience E.

Components of Learning System

Performance Element:

The performance element is the agent that acts in the world .It percepts and decides on external actions.

Learning Element:

It responsible for making improvements, takes knowledge about performance element and some feedback ,determines how to modify performance element.

Critic:

It tells the learning element how agent is doing by comparing with the fixed standard of performance.

Problem Generator:

This component suggests problems or actions that will generate new examples or experience that helps the system to train further.

Let us see the role of each component with an example.

Example: Automated Taxi on city roads

Performance Element:consists of knowledge and procedures for driving actions.

eg:turning ,accelerating,breaking are the performance elements on roads.

Learning Element:It formulates goals.

Eg:learn rules for breaking,accelerating,learn geography of the city.

Critic: Observes world and passes information to learning element.

Eg:quick right turn across three lanes of traffic ,observe reaction of other drivers.

Problem Generator: Try south city road

Learning Paradigm:

•         Rote learning 

•         Induction

•         Clustering

•         Analogy

•         Discovery

•         Genetic algorithms

•         Reinforcement

Rote Learning:

Rote learning technique avoids understanding the inner complexities but focuses on memorizing the material so that it can be recalled by the learner exactly the way it read or heard.

Learning by memorization: which avoids understanding the inner complexities the subject that is being learned.

           

Learning something from Repeating:saying the same thing and trying to remember how to say it;it does not help to understand ,it helps to remember ,like we learn a poem,song ,etc. 

 

There are two  types of inductive learning,

·         Supervised

·         Unsupervised

Supervised learning:( The machine has access to a teacher who corrects it.)

 learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). Example : Face recognition

  

     Unsupervised Learning:( No access to teacher. Instead, the machine must search for “order” and “structure” in the environment.)

since there is no desired output in this case that is provided therefore categorization is done so that the algorithm differentiates correctly between the face of a horse, cat or human (clustering of data)

Clustering:

In clustering or unsupervised learning, the target features are not given in the training examples. The aim is to construct a natural classification that can be used to cluster the data. The general idea behind clustering is to partition the examples into clusters or classes. Each class predicts feature values for the examples in the class. Each clustering has a prediction error on the predictions. The best clustering is the one that minimizes the error.

Example: An intelligent tutoring system may want to cluster students' learning behavior so that strategies that work for one member of a class may work for other members.

Reinforcement Learning:

     Imagine a robot that can act in a world, receiving rewards and punishments and determining from these what it should do. This is the problem of reinforcement learning.Most  Reinforcement Learning research is conducted with in the mathematical framework of Markov Decision Process.