Miroslav Kubat

An Introduction to Machine Learning (eBook, PDF)

• Format: PDF

Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 458
• Erscheinungstermin: 25. September 2021
• Englisch
• ISBN-13: 9783030819354
• Artikelnr.: 62636386

Autorenporträt

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. Professor Kubat is also known for his many practical applications of machine learning, ranging from oil-spill detection in radar images to text categorization to tumor segmentation in MR images.

Inhaltsangabe

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.

Rezensionen

"Miroslav Kubat's Introduction to Machine Learning is an excellent overview of a broad range of Machine Learning (ML) techniques. It fills a longstanding need for texts that cover the middle ground of neither oversimplifying nor too technical explanations of key concepts of key Machine Learning algorithms. ... All in all it is a very informative and instructive read which is well suited for undergraduate students and aspiring data scientists." (Holger K. von Joua, Google+, plus.google.com, December, 2016)

"It is superbly organized: each section includes a 'what have you learned' summary, and every chapter has a short summary, accompanying (brief) historical remarks, and a slew of exercises. ... In most of the chapters, there are very clear examples, well chosen and illustrated, that really help the reader understand each concept. ... I did learn quite a bit about very basic machine learning by reading this book." (Jacques Carette, Computing Reviews, January, 2016)