
- 217 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
About this book
This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic models of learning, tools that crisply classify what is and is not efficiently learnable. After a general introduction to Valiant's PAC paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores specific topics such as finite automata and neural networks. The presentation is intended for a broad audience--the author's ability to motivate and pace discussions for beginners has been praised by reviewers. Each chapter contains numerous examples and exercises, as well as a useful summary of important results. An excellent introduction to the area, suitable either for a first course, or as a component in general machine learning and advanced AI courses. Also an important reference for AI researchers.
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Yes, you can access Machine Learning by Balas K. Natarajan in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Front Cover
- Machine Learning: A Theoretical Approach
- Copyright Page
- Table of Contents
- Chapter 1. Introduction
- Chapter 2. Learning Concepts on Countable Domains
- Chapter 3. Time Complexity of Concept Learning
- Chapter 4. Learning Concepts on Uncountable Domains
- Chapter 5. Learning Functions
- Chapter 6. Finite Automata
- Chapter 7. Neural Networks
- Chapter 8. Generalizing the Learning Model
- Chapter 9. Conclusion
- Index