 :/LC.GIF&client=easterncarolinaedu)
Machine learning for science and engineering. / Herman Jaramillo, Andreas Rüger ; Umair bin Waheed, managing editor.
| Author/creator | Jaramillo, Herman author. |
| Other author | Rüger, Andreas, author. |
| Other author | Waheed, Umair bin, editor. |
| Other author | Society of Exploration Geophysicists, issuing body. |
| Format | Electronic |
| Publication | Houston, TX : Society of Exploration Geophysicists, 2023. |
| Description | 1 online resource (x, 407 pages) : illustrations (black and white, and colour). |
| Supplemental Content | https://go.openathens.net/redirector/ecu.edu?url=https://doi.org/10.1190/1.9781560803898 |
| Supplemental Content | GeoScienceWorld |
| Subjects |
| Series | Course notes series ; no. 17 Course notes series ; no. 17. ^A1403467 |
| Summary | This work teaches the underlying mathematics, terminology, and programmatic skills to implement, test, and apply machine learning (ML) to real-world problems. It builds the mathematical pillars required to comprehend and master modern ML concepts and translates the newly gained mathematical understanding into better applied data science. |
| Bibliography note | Includes bibliographical references and index. |
| Spec. audience char. | Specialized. |
| Source of description | Description based on online resource; title from PDF title page (viewed on July 15, 2024). |
| Issued in other form | Print version : 9781560803881 |
| ISBN | 9781560803898 (ebook) : |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | ✔ Available |