Designing a Learning System

What is Machine Learning Introduction Well-Posed Learning Problems Designing a Learning System Steps for Designing Learning System Are Perspectives and Issues in Machine Learning Concept Learning and the General to Specific Ordering Concept Learning Task Concept Learning as a Search Important Representation Inductive Bias

Decision Tree Learning Types of Decision Tree Strengths and Weakness of Decision Tree Approach Appropriate Problems in Decision Tree Learning Issues in Decision Tree Learning What is Hypothesis Types of Hypothesis ID3 Information Gain for Each Feature

Artificial Neural Networks I Biological Motivation Neural Network Representations Appropriate Problems for Neural Network Learning Perceptron Genetic Algorithms Application Areas What is Reinforcement Learning Terms Used in Reinforcement Learning Approaches to Implement Reinforcement Learning Temporal Difference Learning EM Algorithm GIBS Algorithm

Designing a Learning System

Previous Page

Next Page

Designing a Learning System

Machine Learning enables a Machine to automatically learn from Data, Improve performance from an Experience and predict things without explicitly programmed.

When we input the Training Data to Machine Learning Algorithm, this algorithm will produce a mathematical model and with the help of the mathematical model, the machine will make a prediction and take a decision without being explicitly programmed. Also, during training data, the more machine will work with it the more it will get experience and the more it will get experience the more efficient result is produced.

Example:

In Spam E-Mail detection Task, T: To classify mails into Spam or Not Spam. Performance measure, P: Total percent of mails being correctly classified as being “Spam” or “Not Spam”. Experience, E: Set of Mails with label “Spam”.

Previous Page

Next Page

Designing a Learning System

MACHINE LEARNING

DECISION TREE LEARNING

ARTIFICIAL NEURAL NETWORKS-I

Designing a Learning System