| dc.contributor.advisor | Kalyan Veeramachaneni. | en_US |
| dc.contributor.author | Ravikumar, Akshay | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2018-12-18T19:47:11Z | |
| dc.date.available | 2018-12-18T19:47:11Z | |
| dc.date.copyright | 2018 | en_US |
| dc.date.issued | 2018 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/119720 | |
| dc.description | Thesis: M. Eng. in Computer Science, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. | en_US |
| dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
| dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
| dc.description | Includes bibliographical references (page 81). | en_US |
| dc.description.abstract | In this thesis, we present DeepMining, a framework to search for machine learning pipelines. The high-level goal of DeepMining is to solve the pipeline search problem: given a problem and a dataset, find the pipeline best-suited to solve that problem. The DeepMining platform serves as a testbed for developers to experiment with different methods of computing and evaluating machine learning pipelines. Specifically, developers have autonomy over how to evaluate different configurations in parallel, score a pipeline given a dataset and hyperparameter configuration, and efficiently search over the pipeline space. DeepMining was designed with modularity and extensibility in mind: developers can easily implement new search algorithms, scoring functions, and computation frameworks. At the same time, users can switch between these modules with minimal effort. | en_US |
| dc.description.statementofresponsibility | by Akshay Ravikumar. | en_US |
| dc.format.extent | 81 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | A framework to search for machine learning pipelines | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M. Eng. in Computer Science | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.identifier.oclc | 1078638316 | en_US |
