Artificial Neural Networks I

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

Artificial Neural Networks I

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Introduction

Artificial neural networks (ANNs) provide a general, practical method for learning real-valued, discrete-valued, and vector-valued functions from examples. Algorithms such as BACKPROPAGATION gradient descent to tune network parameters to best fit a training set of input-output pairs. ANN learning is robust to errors in the training data and has been successfully applied to problems such as:

Interpreting visual scenes
Speech recognition
Learning robot control strategies.

Neural network learning methods provide a robust approach to approximating real-valued, discrete-valued, and vector-valued target functions. For certain types of problems, such as learning to interpret complex real-world sensor data, artificial neural networks are among the most effective learning methods. The BACKPROPAGATION algorithm has proven surprisingly successful in many practical problems such as learning to recognize handwritten characters to recognize spoken words to recognize faces.

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Artificial Neural Networks I

MACHINE LEARNING

DECISION TREE LEARNING

ARTIFICIAL NEURAL NETWORKS-I

Artificial Neural Networks I