3. MACHINE LEARNING FRAMEWORK:

 ·       y = f(x)

o   y = output, x = feature representation, f( ) is prediction function

·       Training: Given a training set, estimate the prediction function f( ) by minimizing the prediction error.

·       Testing: Apply f ( ) to unknown test sample x and predicted value (output) is y.

·       y = f(w, x) (for linear model)

o   y = output, x = feature representation, w = weight, f(w, ) = prediction function

·       Training: Given a training set, estimate the prediction function, f( ) by minimizing the prediction error.

·       Testing: Apply f(w, ) to unknown test sample x and predicted value (output) is y

o   Parameter: primary problem is to find the parameters W.

AI/ML Problem – phases:

The following are the various steps involved in problem solving using AI/ML:

·       Define your task

·       Collect Data

·       Preprocessing of Data

·       Dimensionality Reduction/Feature Selection

·       Choose ML Algorithm

·       Experimental Design

·       Test and Validate

·       Run System

MACHINE LEARNING WORK FLOW: