Text| Item type | Current library | Item location | Collection | Shelving location | Call number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|---|---|---|
| Books | Vikram Sarabhai Library | Rack 28-B / Slot 1409 (0 Floor, East Wing) | Non-fiction | General Stacks | 519.5 B3S8 (Browse shelf(Opens below)) | Available | 206813 |
The third edition considers significant advances in recent years, among which are:
the development of overarching, conceptual frameworks for statistical learning;
the impact of “big data” on statistical learning;
the nature and consequences of post-model selection statistical inference;
deep learning in various forms;
the special challenges to statistical inference posed by statistical learning;
the fundamental connections between data collection and data analysis;
interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy.
This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research. Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.
https://link.springer.com/book/10.1007/978-3-030-40189-4
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