<p>Lecture, three hours; discussion, one hour. Requisites: courses 100A, 212A. Elements of statistical learning. Topics include nonlinear regression, regression trees, support vector machines, unsupervised learning, and deep learning. Letter grading.</p>

Biostatistics

Jonathan and Karin Fielding School of Public Health

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BIOSTAT 212B - Statistical Learning B

Statistical Learning B

MS3: Employ computational methods of applied regression to analysis of biomedical data sets

Construct and diagnose nonlinear regression, regression trees, support vector machines, unsupervised learning methods, and deep learning models in R and Python.

MPH1: Demonstrate mastery of fundamental concepts of statistical analysis for datasets from health studies.

MS5: Communicate results of biostatistical research both orally and in writing

In homework assignments, students will practice R and Python solutions to various statistical learning tasks on biomedical and public health data sets.

MS2: Examine foundations of linear and generalized linear statistical models.

Assess the scientific or public health objectives of a data analysis and recommend a modeling strategy to meet the objectives.

Demonstrate understanding of the theory of nonlinear regression, regression trees, support vector machines, unsupervised learning, and deep learning.

MPH5: Prepare written and oral presentations providing public health insights based on statistical analyses

Prepare a report synthesizing the results of analysis

MPH2: Employ computational methods for analysis of public data sets.

General Catalog

Statistical Learning B

BIOSTAT 212B

4 units