OR 750. Bayesain Learning

Department of Systems Engineering and Operations Research
George Mason University
Spring 2021

Videos of Lectures

Course Material

Course Number: OR 750
Location: Zoom Thu 4:30-7:10pm
Instructor: Vadim Sokolov (vsokolov(at)gmu.edu)
Office hours: by Appointment
Prerequisites: Undergraduate Calculus, probability theory, statistics, computer programming skills (ideally R).
HW Logistics: You will submit your HW and projects to BlackBoard

Content and goals

This is a graduate course on Bayesain learning. Although basics will be revisited, the pace will be swift so we can get to advanced topics as quickly as possible. This course details classical Bayesain techniques as well as modern approaches from both statistics and machine learning. We will consider some canonical examples of Bayesain analysis but will concentrate on modern Bayesain techniqes, computation and implementation, as well as modern applications. The course material will emphesize deriving and implementing methods over proving theoretical results.
During weeks 11-15, this class will be run in a seminar mode. A student or the instructor will lead the discussion.

Tentative Schedule/List of Topics


Rubric: 30% HW, 10% Discussion, 60 % Final Project. No in-class examination. Cutoffs: A: 90, B: 80, C: 70, F: < 70

Optional Texts


Mason Honor Code

To promote a stronger sense of mutual responsibility, respect, trust, and fairness among all members of the George Mason University community and with the desire for greater academic and personal achievement, we, the student members of the university community, have set forth this honor code: Student members of the George Mason University community pledge not to cheat, plagiarize, steal, or lie in matters related to academic work. Students are responsible for their own work, and students and faculty must take on the responsibility of dealing with violations. The tenet must be a foundation of our university culture.

All work performed in this course will be subject to Mason's Honor Code. Students are expected to do their own work in the course. For the group project, students are expected to collaborate with their assigned group members. In papers and project reports, students are expected to write in their own words,

Individuals with Disabilities

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