CMPT 440 Outline

COURSE NUMBER:

CMPT 440

COURSE TITLE:

Computer Modelling

NAME OF INSTRUCTOR:

Dr. Robert MacDonald

CREDIT WEIGHT AND WEEKLY TIME DISTRIBUTION:

credits 3(hrs lect 3 - hrs sem 0 - hrs lab 3)

COURSE DESCRIPTION:

An introduction to the use of computer modelling. This course will emphasize the usefulness and limitations of computer simulations and modelling in drawing inferences. Projects will be taken from a variety of topics and will be coordinated with faculty from other disciplines. Students who do not have the necessary prerequisites but can demonstrate a sufficient mathematical proficiency and computing competency can obtain consent from the instructor to enrol in this course.

Prerequisites: CMPT 420

REFERENCE TEXTS:

W.H. Press, S.A. Teukolsky, W.T. Vetterling, & B.P. Flannery (2007).Numerical Recipes: The art of scientific computing (3rd ed.) Cambridge University Press.
D. Maki & M. Thompson (2006). Mathematical Modeling and Computer Simulation. Thomson Brooks/Cole.
N. Yau (2011). Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics. Wiley.

MARK DISTRIBUTION IN PERCENT:


Individual Laboratory Project	25%
Term Laboratory Projects (2 projects)	50%
Research Paper Summary & Oral Presentation	25%
	100%

COURSE OBJECTIVES:

This course is designed to allow students to become familiar with typical problems faced by Computing Scientists as they model complex events and phenomenon. Projects will vary and will be drawn from a number of different disciplines. Typical projects may include topics such as the modelling of population growth, economic growth, the spread of a disease, the reaction of a chemical, the dynamics of a thrown object in any sport, to the modelling of the expansion of the Universe, the swing of a pendulum, or the motion of a spring. Students will be required to understand the process of modelling from mathematical formulation of the problem, to implement their numerical analysis knowledge. As well, student will be required to present their model and their computation outcomes all with the intention of educating their peers regardingthe chosen methodology for their chosen problem.

COURSE TOPICS:

Particle tracking (difference equations, uncertainties, and statistics)
Retail staffing (queuing systems)
Chemical reactions (Markov chains)
3D rendering (Monte Carlo integration)
Video game AI (finite state machines)
Epidemiology (cellular automata)

Required texts, assignments, and grade distributions may vary from one offering of this course to the next. Please consult the course instructor for up to date details.