BIOST 2079 - INTRODUCTORY STATISTICAL LEARNING FOR HEALTH SCIENCES

Minimum Credits: 2
Maximum Credits: 2
This 2-credit course is a graduate level course to introduce basic concept and methods for statistical learning with emphasis on modern health science applications. The syllabus includes linear regression with regularization, supervised machine learning, unsupervised clustering, dimension reduction and other special topics (e.g. Bayesian network and hidden Markov model). Target audience will be second year Biostatistics master students or early PhD students with interests in statistical learning techniques for health science data. Through homework problem sets, computer labs and a final project, students train with hands-on materials to implement methods and interpret results in real applications.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade
Course Requirements: PREQ: BIOST 2039 and 2043 and 2049; PLAN: Biostatistics(MS or PHD); Students are expected to have programming experiences in R or in some low-level languages such as C, C++, Java and Fortran.

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