The Caltech-JPL Summer School on Big Data Analytics

The Caltech-JPL Summer School on Big Data Analytics

Course
en
English
40 h
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Source
  • From www.coursera.org
Conditions
  • Self-paced
  • Free Access
More info
  • 2 Sequences
  • Introductive Level

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Course details

Syllabus

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.

1. Introduction to the school.  Software architectures.  Introduction to Machine Learning.

2. Best programming practices.  Information retrieval.

3. Introduction to R.  Markov Chain Monte Carlo.

4. Statistical resampling and inference.

5. Databases.

6. Data visualization.

7. Clustering and classification.

8. Decision trees and random forests.

9. Dimensionality reduction.  Closing remarks.

Prerequisite

None.

Instructors

  • 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

Editor

Caltech

Platform

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