A computational approach to statistical learning/ Taylor Arnold, Michael Kane and Bryan W Lewis
Series: Texts in Statistical SciencePublication details: Boca Raton: CRC Press, 2019Description: xiii, 361 pages, 22.5 cmISBN:- 9780367494049
- 23 006.31015195 Ar658
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 006.31015195 Ar658 (Browse shelf(Opens below)) | Available | 138493 |
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| 006.31 Z63 Machine learning applications in software engineering | 006.31015 D329 Mathematics for machine learning/ | 006.3101515 Si593 Mathematical analysis for machine learning and data mining/ | 006.31015195 Ar658 A computational approach to statistical learning/ | 006.3101570 R178 Deep learning for the life sciences: applying deep learning to genomics microscopy drug discovery and more/ | 006.310727 C495 On adversarial robustness of deep learning systems/ | 006.312 Ad234 Data analysis and pattern recognition in multiple databases / |
Includes bibliographical references and index
1. Introduction -- 2. Linear Models -- 3. Ridge Regression and principal component analysis -- 4. Linear Smoothers -- 5. Generalized linear models -- 6. Additive models -- 7. Penalized regression models -- 8. Neural networks -- 9. Dimensionality reduction -- 10. Computation in practice -- A Linear algebra and matrices -- B Floating point arithmetic and numerical computation
A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.
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