**GEORGE MASON UNIVERSITY**

**DEPARTMENT OF COMPUTER SCIENCE **

CS 700 - QUANTITATIVE METHODS & EXPERIMENTAL DESIGN IN CS

SPRING 2014

Prof. Sanjeev Setia

setia at gmu.edu

703-993-4098

## Description

Prerequisites: STAT 344 (or
equivalent), at least two
600 level course offered by the CS department, 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.

## Readings

**Textbook:**

David Lilja, Measuring Computer Performance: A Practitioner's Guide, Cambridge University Press, 2005. ISBN: 05216-4670-7.

**Other recommended books:**
- Raj Jain,
*The Art of
Computer Systems Performance Analysis*, John Wiley, 1991, ISBN:
0-471-50336-3. Please download the errata
- 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.
Almeida, Capacity
Planning for Web Services: metrics, models, and methods, Prentice
Hall, 2002.

## Course Outline

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

## Grading

The grade for the course will be based on the following components:
(i) Homework Assignments (35%) (ii) Class Project (15%) (iii) Mid-term
exam (25%) (iii) Take home
final exam
(25%).

**Important Dates**

**
Mid-term exam:** March 27 (tentative)

**Project Presentation :** May 1

## Class Project

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

Office hours will be on Thursday from 3-4 pm in my office (Room 4300, Engineering Building), or by appointment.

## Course Material

All handouts and other course material will be available at the Blackboard page for the class.