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size
23.62 x 15.24 x 3.56 cm
Product Information
Ideal For
General Readers, Fiction Lovers, Mythology & Historical Fiction Readers, Indian Mythology Enthusiasts, Young Adults (YA) & Adults, College Students, Competitive Exam Aspirants (General Reading / Culture), Gift Purpose (Books Gift Set), Spiritual & Philosophy Interest Readers, Library / Book Collection Buyers
Country Of Origin
India
About Author
Robert Tibshirani (Born July 10, 1956) Is A Professor In The Departments Of Statistics And Health Research And Policy At Stanford University. He Was A Professor At The University Of Toronto From 1985 To 1998. In His Work, He Develops Statistical Tools For The Analysis Of Complex Datasets, Most Recently In Genomics And Proteomics.
Author Name
Robert Tibshirani
Genre
Science & Mathematics
No Of Pages
745
Publisher
Anant Pai / Amar Chitra Katha
Isbn Code
978-0387848570 (Isbn Code), 978-0387848570 (Isbn Code)
Product Description
- Premium Quality: This hardcover edition of The Elements of Statistical Learning by Robert Tibshirani spans 745 pages and is a valuable resource for statisticians and anyone interested in data mining in science or industry.
- Product Design: The book describes important ideas across a variety of fields such as medicine, biology, finance, and marketing within a common conceptual framework, supported by a liberal use of colour graphics.
- User Experience: While the approach is statistical, the emphasis is on concepts rather than mathematics, making complex topics accessible through many illustrative examples presented with clarity and rigour.
- Versatile Occasion: A broad resource covering both supervised learning and unsupervised learning, suitable for academic study, professional reference, and gifting to data science or statistics practitioners.
- Quality Assurance: Authored by Robert Tibshirani, Professor in the Departments of Statistics and Health Research and Policy at Stanford University, and a leading authority on statistical tools for complex datasets.
- Lifestyle Essential: Offering the first comprehensive treatment of topics such as neural networks, support vector machines, classification trees, and boosting in any single book, this title is an indispensable reference for the modern data scientist.