Department of Electrical and Computer Engineering
University of Massachusetts/Amherst
ECE 697SM: Stochastic Methods for Dense Networks
Overview: This course is a graduate-level introduction to stochastic methods in dense network analysis. This course covers some interesting and useful tools in stochastic analysis, and could be helpful in advancing students own research projects. It is also useful to prepare students for their career in broadening their knowledge scope into other than their own disciplines. Although the content is advanced, the treatment is quite basic. Familiarity with basic probability theory, linear algebra and ordinary differential equations would suffice as prerequisite. There will be 6-8 homework sets, a mid term exam, and a term paper.
Objectives: Students completing this course will gain substantial knowledge about stochastic analysis tools in dense network analysis. The course is designed to help students for their own research projects as well as to prepare them for the future development.
Prerequisites: Familiarity with basic probability theory, linear algebra and ordinary differential equation.
Instructor: Weibo Gong
Phone: (413) 545-0384; email: firstname.lastname@example.org
Lectures: Marcus 106-108 (studio A); Time TBD.
Office Hours: TBD
Textbook: Lecture Notes
Grading policy: Homework: 20%
Midterm Exam: 30%
Term Paper: 50%
Topics plan to cover: