Machine Learning

Learning agent

Any other agent

Learning agent

learning element: responsible for making improvementsperformance element : responsible for selecting external actionscritic : gives feedback to the learning element on how the agent is doing with respect to a fixed performance standard and determines how the performance element should be modified to do better in the future.problem generator : responsible for suggesting actions that will lead to new and informative experiences

Structural organization of levels in biological nervous systems.

Human brain

Artificial neurons

Neurons work by processing information.

The McCullogh-Pitts model

Inputs

Outputw2

w1

w3

wn

wn-1

. . .

x1

x2

x3

…

xn-1

xn

y)(;

1

zHyxwzn

iii

Artificial neural network (ANN)is a mathematical model or computational model based on biological neural networks Artificial Neural Network consists of neurons arranged in layers Neurons act as parallel processorNeurons are connected with each other vi connection.there are weights associated with connectionsImplementation:

Learningtesting

Artificial neural networks

Inputs

Output

An artificial neural network is composed of many artificial neurons that are linked together according to a specific network architecture. The objective of the neural network is to transform the inputs into meaningful outputs.

Artificial neural networks

Dendrites: Input Layer

Axon : Output Layer

Soma: Net( weighted sum of input y_in) and activation function

Synapse: Weights

Why use ANN?-Adaptive learning: An ability to learn how to do tasks based on

the data given for training or initial experience.

-Self-Organization: An ANN can create its own organization or representation of the information it receives during learning

time.

-Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and

manufactured which take advantage of this capability.

-Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation

of performance. However, some network capabilities may be retained even with major network damage.

ANN Characterization

ANN can be characterized by:Activation function

Weights Adjustment (learning algorithm)

Architecture

Learning Algorithm

Learning in ANN is Weights adjustment to get desired output

To minimize the error

To gain more experience

LearningSupervised

unsupervised

Supervised Learning

There is supervisor during learning processInput and output are knownThe job of ANN is to classify any new input according to known classesExample : teaching baby the difference pens and other thingsLVQ (learning vector quantization)

Unsupervised learning

Input known but output unknown

The classes are unknown to ANN

Job of ANN is to find similarities between input and divide them into categories (cluster)

SOM (Self organizing map)

Architecture

Feed forwardallow signals to travel one way only; from input to output.

There is no feedback (loops)

Multi layer

Architecture

Feedback networks signals travelling in both directions by introducing loops in the network

Decision Tree Representation

Outlook

Humidity Wind

SunnyOvercast

Rain

High Normal Strong Weak

Decision Tree for the concept PlayTennis

Pattern recognition system.

Flow chart of machine learning for pattern recognition.

Bayes classifier

Approaches

What is a Concept?

A Concept is a a subset of objects or events defined over a larger set [Example: The concept of a bird is the subset of all objects (i.e., the set of all things or all animals) that belong to the category of bird.]

Things

Animals

Birds

Cars

What is Concept-Learning?

Given a set of examples labeled as members or non-members of a concept, concept-learning consists of automatically inferring the general definition of this concept.

In other words, concept-learning consists of approximating a boolean-valued function from training examples of its input and output.