BIOSTAT 207 - Statistical Methods For Learning And Discovery

Institution:

Duke University

Subject:

BIOSTATISTICS

Description:

This course surveys a number of techniques for high dimensional data analysis useful for data mining, machine learning and genomic applications, among others. Topics include principal and independent component analysis, multidimensional scaling, tree based classifiers, clustering techniques, support vector machines and networks, and techniques for model validation. Core concepts are mastered through the analysis and interpretation of several actual high dimensional genomics datasets. Prerequisites: BIOSTAT 201 through BIOSTAT 206, or their equivalents. Credit: 3

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

WWW.DUKE.EDU

Phone Number:

(919) 684-8111

Regional Accreditation:

Southern Association of Colleges and Schools

Calendar System:

Semester

The Course Profile information is provided and updated by third parties including the respective institutions. While the institutions are able to update their information at any time, the information is not independently validated, and no party associated with this website can accept responsibility for its accuracy.