Understanding Machine Learning:From theory to algorithms
By: Shalev-Shwartz,Shai.
Contributor(s): Ben-David,Shai.
Material type:
BookPublisher: New Delhi Cambridge University Press 2020Description: 397.ISBN: 978-1-107-51282-5.Subject(s): Machine LearningDDC classification: 006.31
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BSDU Knowledge Resource Center, Jaipur General Stacks | 006.31 SHA (Browse shelf) | Available | 018046 |
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| 006.31 DUT Machine Learning | 006.31 MIT Machine Learning | 006.31 MIT Machine Learning | 006.31 SHA Understanding Machine Learning:From theory to algorithms | 006.312 BHA Data Mining and Data Warehousing:Principles and Practical Techniques | 006.312 GOS Data Mining and Data Warehousing | 006.312 JAI Big Data & Hadoop |
DescriptionContentsResourcesCoursesAbout the Authors
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
This book is written for the first year students of Engineering– A blend of theory and solved problems will equip the students with the fundamental knowledge and application of the coding concepts. This will nurture them to have a strong foundation for the courses in the subsequent semesters.
This book ensures a smooth and successful transition to being a skilled Python expert. The book uses a simple-to-complex and easy-to-learn approach throughout. The concept of ‘learning by-solving has been stressed everywhere in the book. Each feature of Python is treated in depth followed by a complete program example to illustrate its use. Wherever necessary, concepts are explained pictorially to facilitate better understanding. The book presents a contemporary approach to programming, offering a combination of theory and practice.
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