140.644.01 | AY 2013-2014 - 3rd Term | 4 Credit(s)
MW 10:30:00 AM
  • Contact Information
  • Course Learning Objectives

    Upon successfully completing this course, students will be able to:

    • describe methods to evaluate statistical machine learning models.
  • Course 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.

  • Intended Audience

    Public health students and faculty from outside the Department of Biostatistics who have an understanding of basic statistical principles

  • Methods of Assessment

    Grading Policy: Homework.

    Grading Restrictions: Letter grade

  • Prerequisites

    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.

  • Academic Ethics Code

    The code, discussed in the Policy and Procedure Memorandum for Students, March 31, 2002, will be adhered to in this class:

    Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.

  • Disability Support Services

    If you are a student with a documented disability who requires an academic accommodation, please contact Betty H. Addison in the Office of Student Life Services:, 410-955-3034, or 2017 E. Monument Street.