An Introduction to Machine Learning

Offers frequent opportunities to practice techniques with control questions, exercises, thought experiments, and computer assignments.

Reinforces principles using well-selected toy domains and relevant real-world applications.

Provides additional material, including an instructor's manual with presentation slides, as well as answers to exercises.

Autorentext

Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 conferences and workshops, and is an editorial board member of three scientific journals. He is widely credited with co-pioneering research in two major branches of the discipline: induction of time-varying concepts and learning from imbalanced training sets. He also contributed to research in induction from multi-label examples, induction of hierarchically organized classes, genetic algorithms, and initialization of neural networks.

Inhalt
1 A Simple Machine-Learning Task.- 2 Probabilities: Bayesian Classifiers.- Similarities: Nearest-Neighbor Classifiers.- 4 Inter-Class Boundaries: Linear and Polynomial Classifiers.- 5 Artificial Neural Networks.- 6 Decision Trees.- 7 Computational Learning Theory.- 8 A Few Instructive Applications.- 9 Induction of Voting Assemblies.- 10 Some Practical Aspects to Know About.- 11 Performance Evaluation.- 12 Statistical Significance.- 13 Induction in Multi-Label Domains.- 14 Unsupervised Learning.- 15 Classifiers in the Form of Rulesets.- 16 The Genetic Algorithm.- 17 Reinforcement Learning.