02738nam a22002297a 4500999001500000003000400015005001700019008004100036020002200077028005800099040002400157082001600181100002500197245006100222260004800283300000800331504199200339650002102331700001902352942001202371952012502383  c2479d2479OSt20200224142453.0200118b           ||||| |||| 00| 0 eng d  a978-1-107-51282-5  bAllied Informatics, Jaipurc7084d13/01/2020q2019-20  aBSDUbEnglishcBSDU  a006.31bSHA  aShalev-Shwartz,Shai   aUnderstanding Machine Learning:From theory to algorithms  aNew DelhibCambridge University Pressc2020  a397  aDescriptionContentsResourcesCoursesAbout 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.  aMachine Learning  aBen-David,Shai  2ddccBK  00102ddc4070aBSDUbBSDUcGENd2020-01-18g995.00l0o006.31 SHAp018046r2020-02-12 00:00:00v995.00w2020-01-18yBK