BIOST 2080 - ADVANCED STATISTICAL LEARNING

Minimum Credits: 2
Maximum Credits: 2
This is a 2-credit course in advanced statistical learning, covering topics related to the statistical interpretation and theory behind machine learning models/methods. Emphases will be given to in-depth derivation of models/algorithms from topics covered in BIOST 2079 (Introductory Statistical Learning for Health Sciences) as well as additional topics on modern statistical learning methodologies, with special focus on methods for health science applications. This course is designed for graduate students in the Department of Biostatistics and other interested graduate students who already have sufficient statistical and programming background. Students are expected to be familiar with R. Experience in C/C++, Python or Matlab may be helpful, but is not required. Programming skills/training shall be demonstrated by previous programming (or programming heavy) courses in R, Python, Matlab, C/C++, etc.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade
Course Requirements: PREQ: BIOST 2049 and BIOST 2079; Special Instructions: Programming skills/training shall be demonstrated by previous programming (or programming heavy) courses in R, Python, Matlab, C/C++, etc.

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