02550nam a22001937a 4500003000400000005001700004008004100021020002200062028005800084040002400142082001600166100002500182245006100207260004800268300000800316504199200324650002102316700001902337OSt20200224142453.0200118b           ||||| |||| 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