What is Stochastic Computing?
Monte Carlo methods are numerical methods that use random numbers to compute quantities of interest. This is normally done by creating a random variable whose expected value is the desired quantity. One then simulates and tabulates the random variable and uses its sample mean and variance to construct probabilistic estimates.
Purpose of the Course:
This course presents the fundamentals of the Monte Carlo method, or as it was originally known, the "method of statistical sampling." This course is meant to take mathematically and computationally mature students and given them a very comprehensive introduction to Monte Carlo methods. The material in this course focuses on:
Developing the mathematical background required for understanding and developing Monte Carlo methods. This is primarily probability theory, statistics, the theory of stochastic processes, and connections with functional analysis.
Examining Monte Carlo methods from the point-of-view of numerical methods and computing. It should be recalled that Monte Carlo methods developed considerably at the down of the age of electronic computing, and they have blossomed since as potent computational techniques.
Reflecting on Monte Carlo methods through specific applications. There are few areas of science and technology where Monte Carlo methods are not used, and understanding how they are used in applications, with an eye on cross-domain fertilization, is a very important way to motivate and reinforce interest in Monte Carlo Methods.
By combining the theoretical background, with the
computational approach firmly anchored in applications, the Monte
Carlo method can be used to solve many problems that traditional
deterministic methods cannot. In addition, there are a wide
number of areas where both Monte Carlo and deterministic methods
can be used, where the Monte Carlo approach offers a very
attractive alternative. Thus, Monte Carlo methods themselves
are a fruitful source of research problems, and when combined with
deterministic methods have the promise to provide many improved
numerical methods.
Lecture Notes for the Course:
Can be found here.
Secondary Purpose of the Course:
The hope is that the process of teaching this course will produce a set of course notes that can be crafted into a good, intermediate to advanced textbook on Monte Carlo methods. Thus, I expect to ask some things of the students that are going to be a bit out of the norm for students in a typical applied mathematics class.
Meeting Time/Place:
9:30AM to 10:45AM: Tuesday and
Thursday on Zoom: https://fsu.zoom.us/my/mascagni
Extra times for lectures and student presentations
will be found based on student
availability.
Prerequisites:
This course is open to graduate students in Mathematics and Computer Science, and is listed with course numbers for each of those department. It is also appropriate for graduate students in Physics or Engineering that are using Monte Carlo methods in their work, as well as for SC graduate students. SC students can take this for "Class C" credits.
Instructor:
Michael Mascagni, Ph.D.
Professor of Computer Science
Professor of Mathematics
Professor of Scientific Computing
Florida State University
Tallahassee, FL 32306-4530
E-mail: mascagni@fsu.edu
Office: Dirac Science Library 498/Love 207
Telephone: +1.850.644.3290
For the curious, here are links to a brief biography of the Professor, and the Professor's home page.
Office Hours:
For this course, my office hours will be held online at the Zoom link above from 11-12AM on
Tuesday and Thursdays. Email me to say you want to come to office hours, as I will work on other things if nobody is coming.
Grading Policy:
The grade will be based on class participation and mostly a project. The project will be of the student's choosing, and will require both a written and oral presentation at the end of the term.
E-mail Policy:
All e-mail communication regarding this
course must be sent and received from an fsu.edu e-mail
address.
Warning and Encouragement:
The students are warned that by taking this course they are implicitly submitting themselves in the role of instructional "Guinea Pigs." As such, your constant input is not only permitted, but strongly encouraged. Feel free to speak up in class, to visit me in my office with your opinions and criticisms, or to e-mail me with ideas on how to improve this class. Also, if something is not clear to you in class, it is very likely unclear to others. So, students are encouraged to interrupt the lectures and ask questions and to come to see me out of class for clarification.
Academic
Honor Policy:
The Florida State University Academic Honor Policy outlines the
University's expectations for the integrity of students' academic
work, the procedures for resolving alleged violations of those
expectations, and the rights and responsibilities of students and
faculty members throughout the process. Students are
responsible for reading the Academic Honor Policy and for living
up to their pledge to ". . . be honest and truthful and . . . [to]
strive for personal and institutional integrity at Florida State
University." (Florida State University Academic Honor
Policy, found at http://dof.fsu.edu/honorpolicy.htm).
Americans with Disabilities Act:
Students with disabilities needing academic accommodation should:
(1) register with and provide documentation to the Student
Disability Resource Center; and
(2) bring a letter to the instructor indicating the need for
accommodation and what type. This should be done during the
first week of class.
This syllabus and other class materials are available in
alternative format upon request:
For more information
about services available to FSU students with disabilities,
contact the:
Student Disability Resource Center
97 Woodward Avenue, South
108 Student Services Building
Florida State University
Tallahassee, FL 32306-4167
(850) 644-9566 (voice)
(850) 644-8504 (TDD)
sdrc@admin.fsu.edu
http://www.disabilitycenter.fsu.edu/