Modeling, Simulation, and Monte Carlo
3.0
creditsAverage Course Rating
Concepts and statistical techniques critical to constructing and analyzing effective simulations; emphasis on generic principles rather than specific applications. Topics include model building (bias-variance tradeoff, model selection,, Fisher information), benefits and drawbacks of simulation modeling, random number generation, simulation-based optimization, discrete multiple comparisons using simulations, Markov chain Monte Carlo (MCMC), and input selection using optimal experimental design.
Spring 2015
Professor: James Spal
The best part of this class was the fact that the instructor made even the least artistically inclined student feel competent by the end of the semester. Students appreciated the opportunity to draw using a variety of media, as well as the lessons on the relationship of the brain to drawing. Some students felt that the three and a half hour weekly sessions were exhausting, and that this class would have been better if there was some focus on drawing portraits as well. Suggestions for improvement included making this course three credits with an H designation to help fulfill overall course requirements. Prospective students should know that no prior art knowledge is necessary.