Advanced courses Fall 2021

The purpose of the AARMS Advanced Course program is to give graduate students and upper year undergraduates from AARMS Member Universities an opportunity to take courses not offered at their home institution free of charge. In Winter 2021, the courses listed below will be offered in an exclusively online format.

Students may be able to claim credit for AARMS Advanced Courses from their home institution using mechanisms similar to the AARMS Summer School. Students hoping to receive academic credit for courses in this program are strongly encouraged to consult with their home institution about this process before the start of lectures.

Both students and instructors should consult the frequently asked questions.  Please send any addition questions about this program to Sanjeev Seahra.

Armin Hatefi (Memorial University)

We will cover some fundamental numerical techniques for statistical inference such as Maximum Likelihood method, Bayesian method, Method of Moments, Optimization and Integration, EM algorithms, Markov Chain and Monte Carlo and other related topics. Some theoretical properties of the methods will also be discussed.

We will extensively use R statistical software which is freely available for Linux, Macintosh and Windows OS. Students should have already taken undergraduate Probability/Mathematical Statistics courses equivalent to STAT 3411 offered at Memorial University and previous exposure to R programming.

David Pike (Memorial University)

The course will focus on concepts and proof techniques pertaining to Graph Theory. Three areas will be covered: matchings (including covers, the Konig-Egervary theorem, Hall’s theorem, Tutte’s 1-factor theorem), connectivity (including edge-connectivity, Menger’s theorem, Dirac’s fan lemma, the Chvatal-Erdos theorem), and network flows (including cuts, the Ford-Fulkerson algorithm, Menger’s theorem).

Previous exposure to basic concepts in Graph Theory, such as from an undergraduate course in the subject, is expected.