BME 5310

BME 5310

Course information provided by the Courses of Study 2018-2019.

Course provides a theoretical foundation for advanced machine learning methods applicable to the analysis of large-scale biomedical data. These applications often have two distinct goals: (1) computation of predictions, and (2) understanding underlying biological mechanisms. A range of examples will be included that cover various data modalities (e.g., DNA sequence data, electronic health record data, images) and different goals. There will be a class project that will involve identifying a data-set, selection of appropriate machine learning problem, writing code and conducting analyses, an oral presentation of results and a written report.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: Linear Algebra (MATH 2940 or equivalent), and Probabilistic Modeling and/or Statistical Analysis (such as BTRY 3010 or CEE 3040). Recommended: CS 1110 or CS 1112 or equivalent; Permission required if prerequisites not met.

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Enrollment Information
Syllabi: 1 available
  •   Regular Academic Session.  Combined with: ECE 5970

  • 3 Credits Graded

  • 12900BME 5310  LEC 001