Decision Tree Learning

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

Decision Tree Learning

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Decision tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.

Decision tree representation

Decision Tree for Playing Tennis

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Decision Tree Learning

MACHINE LEARNING

DECISION TREE LEARNING

ARTIFICIAL NEURAL NETWORKS-I

Decision Tree Learning