Introduction to Machine Learning

Introduction to Machine Learning

Kubat, Miroslav

Springer Nature Switzerland AG

09/2021

458

Dura

Inglês

9783030819347

15 a 20 dias

875

Descrição não disponível.
1. Ambitions and Goals of Machine Learning.- 2. Probabilities: Bayesian Classifiers.- 3. Similarities: Nearest-Neighbor Classifiers.- 4. Inter-Class Boundaries: Linear and Polynomial Classifiers.- 5. Decision Trees.- 6. Artificial Neural Networks.- 7. Computational Learning Theory.- 8. Experience from Historical Applications.- 9. Voting Assemblies and Boosting.- 10. Classifiers in the Form of Rule-Sets.- 11. Practical Issues to Know About.- 12. Performance Evaluation.- 13. Statistical Significance.- 14. Induction in Multi-Label Domains.- 15. Unsupervised Learning.- 16. Deep Learning.- 17. Reinforcement Learning: N-Armed Bandits and Episodes.- 18. Reinforcement Learning: From TD(0) to Deep-Q-Learning.- 19. Temporal Learning.- 20. Hidden Markov Models.- 21. Genetic Algorithm.- Bibliography.- Index.
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Bayesian classifiers;boosting;computational learning theory;decision trees;genetic algorithms;linear and polynomial classifiers;nearest neighbor classifier;neural networks;performance evaluation;reinforcement learning;statistical learning;time-varying classes, imbalanced representation;artificial intelligence;machine learning;data mining;deep learning;unsupervised learning