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Course Catalog

140.644.01 STATISTICAL MACHINE LEARNING: METHODS, THEORY, AND APPLICATIONS

Cancelled

Department:
Biostatistics
Term:
3rd term
Credits:
4 credits
Academic Year:
2012 - 2013
Location:
East Baltimore
Class Times:
  • M W,  10:30 - 11:50am
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Contact:
Han Liu
Course Instructor:

Course Evaluation

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Prerequisite:

Students are expected to be familiar with the following topics to comfortably complete this class. If you do not know these topics, it is your responsibility to do background reading to make sure you understand these concepts.

Description:

Introduces statistical and computational foundations of modern statistical machine learning. Acquaints students with methods to evaluate statistical machine learning models defined in terms of algorithms or function approximations using basic coverage of their statistical and computational theoretical underpinnings. Topics covered include: regression and classification, tree-based methods, overview of supervised learning theory, support vector machines, kernel methods, ensemble methods, clustering, visualization of large datasets and graphical models. Example applications include cancer prognosis from microarray data, graphical models for data visualization and decision making.

Methods of Assessment:

Homework.

Instructor Consent:

No consent required