Special Topic Courses (F2019)

Please contact the instructor if you have questions about prerequisites or other aspects of the course.

Fall 2019

  • ST 590-002: Design of Experiments
    • Instructor: Jon Stallrich
    • Schedule: 11:45am-1:00pm M/W
    • Prerequisties: ST512 or ST514 or ST516
    • Text: TBA
    • Description: The course begins with a comparison of observational studies and randomized, comparative experiments followed by a thorough discussion of design principles. These principles are applied in the discussion of completely randomized designs for an arbitrary set of treatments and then treatments having a factorial structure. The ANOVA approach to analyzing factorial models is discussed in great detail with an emphasis on implementing the appropriate analysis in SAS PROC MIXED. This includes contrast analysis; power and sample size calculations; residual analysis; and randomization-based tests. Complete and incomplete block designs are covered next, followed by block designs with multiple blocking factors such as Latin Squares. Analysis procedures are given assuming fixed and random block effects. The course concludes with more complicated experimental designs such as split plot experiments, confounded block designs, fractional factorials, and response surface methodology. At the end of the course, students should be able to design and analyze traditional experiments and use the intuition gained in the course to design experiments for unconventional experimental conditions.
  • ST 590-018: Statistical Methods II
    • Instructor: Jason Osborne
    • Schedule: 10:15am-11:30am T/TH
    • Prerequisties: ST511 or ST517
    • Text: Statistics for Research, Dowdy, Wearden and Chilko, 3rd edition, ISBN 047126735X
    • Description: This is an applied course that introduces statistical methods based on linear models commonly used to analyze continuous responses in designed experiments. Methods include multiple linear regression, analysis of covariance, general linear models with factorial effects (including simple, main and interaction effects), mixed effects models and multiple comparisons. Experimental designs covered in the course include multi-factor randomized designs, block designs, latin-square designs, completely randomized split-plot designs and randomized complete block split-plot designs.
  • ST 790-001: Introduction to Data Mining and Machine Learning
    • Instructor: Wenbin Lu
    • Schedule: 10:15am-11:30am T/TH
    • Prerequisties: ST701, ST702 or ST521, ST522.
    • Text: The Elements of Statistical Learning, 2nd Edition (Hastie, Tibshirani, Friedman)
    • Description: This course covers modern data science techniques, including basic statistical learning theories and their applications. A variety of data mining methodologies, algorithms, and software tools will be introduced, with emphasis on both conceptual and computational aspects. Applications in bioinformatics, genomics, text mining, social networks, and so on will be covered. This course emphasizes on statistical analysis, methodology, and theory in modern machine learning. It is intended for students who want to practice state-of-art machine learning tools and algorithms, and also understand theoretical principles and statistical properties that underlie the algorithms. The topics include regression, classification, clustering, dimension reduction, and high dimensional analysis. The course is a balance between statistical learning theories and computational methods.
  • ST 810-001: Communicating Science
    • Instructor: Eric Laber
    • Schedule: 4:30pm-5:30pm F
    • Description: A successful career in science depends critically on the ability to explain complex ideas simply and succinctly across a variety of communication media. These media include technical writing, oral presentations, web content, and social media. In this course, we will focus on developing and evaluating communication skills for a career in scientific research. Topics covered include:
      1. reading and writing technical content
      2. creating effective oral presentations
      3. preparing a curriculum vitae and other job application materials
      4. collaboration and consulting

      We will use the department seminars as a means of generating discussion. Students enrolled in this course are required to attend each regular department seminar.