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Fee:
Fee may apply
Programs:
Description
Examine new techniques for predictive and descriptive learning using concepts that bridge gaps among statistics, computer science, and artificial intelligence. This course emphasizes the statistical application of these areas and integration with standard statistical methodology. The differentiation of predictive and descriptive learning will be examined from varying statistical perspectives.
Instructor(s)
Prerequisites
Complete Data Mining and Analysis (Stanford Course:STATS202) or
Introduction to Statistical Learning (Stanford Course:STATS216) with a B+ or better
Topics include
- Classification & regression models
- Multivariate adaptive regression splines
- Prototype & near-neighbor methods
- Neural networks
Note on Course Availability
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.
| Dates: | April 2 - June 4, 2019 |
| Days: | Tue, Thu |
| Times: | 1:30 pm - 2:50 pm |
| Units: | 2.00-3.00 |
| Instructors: | Jerome Friedman |
| Delivery Option: |
Online
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| For Credit |
$2,520.00-$3,780.00
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Provides Stanford University credit that may later be applied towards a graduate degree or certificate. Includes access to online course materials and videos for the duration of the academic quarter. Starting Autumn 2016 there is a $100 fee per course for courses dropped before the drop deadline. Click here for more information about our Registration Policies.
*Note: All amounts shown are in USD |
Notes
Enrollment Dates: February 10 to March 18, 2019This course may not currently be available to learners in some states and territories.
