1 A Simple Machine-Learning Task 1 1.1 Training Sets and Classifiers. 1 1.2 Minor Digression: Hill-Climbing Search. 5 1.3 Hill Climbing in Machine Learning. 9 1.4 The Induced Classifier''s Performance.

12 1.5 Some Diculties with Available Data. 14 1.6 Summary and Historical Remarks. 18 1.7 Solidify Your Knowledge. 19 2 Probabilities: Bayesian Classifiers 22 2.1 The Single-Attribute Case.

22 2.2 Vectors of Discrete Attributes. 27 2.3 Probabilities of Rare Events: Exploiting the Expert''s Intuition. 29 2.4 How to Handle Continuous Attributes. 35 2.5 Gaussian "Bell" Function: A Standard pdf .

38 2.6 Approximating PDFs with Sets of Gaussians. 40 2.7 Summary and Historical Remarks. 43 2.8 Solidify Your Knowledge. 46 3 Similarities: Nearest-Neighbor Classifiers 49 3.1 The k-Nearest-Neighbor Rule.

49 3.2 Measuring Similarity. 52 3.3 Irrelevant Attributes and Scaling Problems. 56 3.4 Performance Considerations. 60 3.5 Weighted Nearest Neighbors.

63 3.6 Removing Dangerous Examples. 65 3.7 Removing Redundant Examples. 68 3.8 Summary and Historical Remarks. 71 3.9 Solidify Your Knowledge.

72 4 Inter-Class Boundaries: Linear and Polynomial Classifiers 75 4.1 The Essence. 75 4.2 The Additive Rule: Perceptron Learning. 79 4.3 The Multiplicative Rule: WINNOW. 85 4.4 Domains with More than Two Classes.

88 4.5 Polynomial Classifiers. 91 4.6 Specific Aspects of Polynomial Classifiers. 93 4.7 Numerical Domains and Support Vector Machines. 97 4.8 Summary and Historical Remarks.

100 4.9 Solidify Your Knowledge. 101 5 Artificial Neural Networks 105 5.1 Multilayer Perceptrons as Classifiers. 105 5.2 Neural Network''s Error. 110 5.3 Backpropagation of Error.

111 5.4 Special Aspects of Multilayer Perceptrons. 117 5.5 Architectural Issues. 121 5.6 Radial Basis Function Networks. 123 5.7 Summary and Historical Remarks.

126 5.8 Solidify Your Knowledge. 128 6 Decision Trees &nbs.