Time/Location: Tuesday 4:307:10, Innovation Hall 208
Instructor: Jana Kosecka
Office hours: 23pm Wednesday
Contact: Office 4444 Research II, email:
kosecka@gmu.edu, 31876
Course web page: http://www.cs.gmu.edu/~kosecka/cs700/
The course covers treatment of the models and practices of experimental computer science and review of tools for applied science in general. The first half of the couse will introduce some elementary methods for statistical analysis of low dimensional (1D2D) data, hypothesis testing, uses of analytic and simulation models, design of performance and quality metrics and interpretation and presentation of experimental results. In the second half we will use mathematical framework of linear algebra to discuss tools for data fitting, singular value decomposition, dimensionality reduction and pattern recognition, which are critical components for analyzing and statistically characterizing higher dimensional data.
Schedule, Homeworks, HandoutsPrerequisites:
Materials:
Required Textbook:
Online resources:
Recommended Textbooks:
Grading:
Tentative List of Topics:

Introduction to Experimental Techniques on CS

Summarizing Measured Data, Comparing Alternatives

Hypothesis testing, Characterization of errors

ANOVA, Confidence Intervals

Introduction to Simulation

Analytical modelling, Regression

Multidimesional data, Dimensionality reduction

Clustering, Introduction to Pattern Recognition
