About the content
This is an intensive, advanced summer school (in the sense used by scientists) in some of the methods of computational, data-intensive science. It covers a variety of topics from applied computer science and engineering, and statistics, and it requires a strong background in computing, statistics, and data-intensive research.
The anticipated schedule of lectures (subject to changes):
Each bullet bellow corresponds to a set of materials that includes approximately 2 hours of video lectures, various links and supplementary materials, plus some on-line, hands-on exercises.
3. Introduction to R. Markov Chain Monte Carlo.
4. Statistical resampling and inference.
6. Data visualization.
7. Clustering and classification.
8. Decision trees and random forests.
9. Dimensionality reduction. Closing remarks.
- Amy Braverman - Jet Propulsion Laboratory (JPL)
- Thomas Fuchs - Jet Propulsion Laboratory (JPL)
- Julian Bunn - Center for Data-Driven Discovery
- Chris Mattmann - Jet Propulsion Laboratory (JPL)
- Santiago Lombeyda - Center for Data-Driven Discovery
- Ciro Donalek
- Daniel Crichton - Jet Propulsion Laboratory (JPL)
- Richard Doyle - Jet Propulsion Laboratory (JPL)
- Ashish Mahabal
- David Thompson - Jet Propulsion Laboratory (JPL)
- Scott Davidoff - Jet Propulsion Laboratory (JPL)
- S. Djorgovski - Astronomy
- Matthew Graham
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