GEORGE MASON UNIVERSITY
DEPARTMENT OF COMPUTER SCIENCE
CS 700 - QUANTITATIVE METHODS & EXPERIMENTAL DESIGN IN CS
Prof. Sanjeev Setia
setia at cs.gmu.edu
Prerequisites: STAT 344 (or
equivalent), at least two
600 level courses in computer science, and doctoral status.
Integrated treatment to the models and practices of experimental
computer science. Topics include scientific methods applied to
computing, workload characterization, forecasting of performance and
quality metrics of systems, uses of analytic and simulation models,
design of experiments, interpretation and presentation of experimental
results, hypothesis testing, and statistical analyses of data. Involves
one or more large-scale projects.
Either of the following books can
be used for the class. Both cover the same material. In some respects,
the Lilja book is more up to date.
Other recommended books:
- Raj Jain, The Art of
Computer Systems Performance Analysis, John Wiley, 1991, ISBN:
0-471-50336-3. Please download the errata
- David Lilja, Measuring Computer Performance: A Practitioner's Guide, Cambridge University Press, 2005. ISBN: 05216-4670-7.
- P. Cohen, Empirical
Methods for Artifical Inteligence, MIT Press, 1995.
- Balachander Krishnamurthy and Mark Crovella, Internet Measurement: Infrastructure, Traffic, And Applications, John Wiley and Sons, Inc., 2006. ISBN: 04700-1461-x
- I. Miller, J. Freund, R.
Johnson, Probability and Statistics for Engineers, Sixth
Edition, Prentice Hall, 2000, ISBN: 0-13-014158-5.
- David M. Levine, Patricia P.
Ramsey, Robert K. Smidt, Applied Statistics for Engineers and
Scientists: Using Microsoft Excel & MINITAB, Prentice Hall,
2001, ISBN: 0134888014.
- Averill M. Law and W. David
Kelton, Simulation Modeling and
Analysis, McGraw Hill, 2000.
- D. A. Menascé and V.
Planning for Web Services: metrics, models, and methods, Prentice
The following topics will be covered (not necessarily in the order below):
- Introduction to Experimental Techniques in CS
- Measurement Tools & Techniques
- Introduction to Simulation
- Summarizing Measured Data
- Comparing Alternatives
- Design of Experiments
- Characterizing Measured Data & Workloads
- Introduction to analytical modeling
The grade for the course will be based on the following components:
(i) Homework Assignments (25%) (ii) Class Project (35%) (iii) Mid-term
exam (20%) (iii) Take home
Mid-term exam: March 19 (tentative)
Project Presentation : April 30
Students will work on an individual project dealing with various
aspects of experimental computer science. Each student will submit a
proposal for an experimental project dealing with a quantitative
analysis of a computer system, algorithm, and/or method of interest to
the student. The deliverables are a technical report and a short
presentation to the class.
Office hours will be on Monday from 3-4 pm in my office (S & T
II Room 430), or by appointment.
Class Home Page
All handouts and other course material will be available at URL