My awesome MEET
students, TA's, co-instructors, & staff
Jerusalem, August 2015
Teaching schedule (academic year 2018-2019): 95-865 "Unstructured Data Analytics" (minis 2 and 3), 94-775 "Unstructured Data Analytics for Policy" (mini 3)
Courses that I have taught for:
New intro-college/advanced-high-school-level course covering introductory probability, probabilistic graphical models, and learning probability distributions. All three of these main topics are covered with heavy emphasis on coding. The course prerequisites are comfort in Python programming and calculus. I developed nearly all the course notes, 75% of the videos, numerous new exercises, all the autograders, and a new two-part final project. This online course is a modified version of the first half of MIT's 6.008 residential course, which I helped develop when 6.008 was still in pilot at MIT. The edX course differs in that its presentation has been made to be accessible to a much broader audience.
New undergraduate core Electrical Engineering and Computer Science course introducing inference and probabilistic graphical models. I taught for the class during the first two semesters that it had ever been offered. I developed substantial portions of the courseware, including Khan Academy style videos for students, a series of Python robot tracking coding projects, new recitation notes, new problem sets, and more.
Update (Fall 2016): Please see the newer Fall 2016 edX course for publicly available notes, videos, exercises, and more.
Introductory graduate-level course on probabilistic graphical models. I made Khan Academy style videos and helped typeset the first complete set of lecture notes for the class. I also delivered three lectures for the Fall 2013 class.
Update (Fall 2014): After my involvement with the course, the lecture notes I helped write were polished by more recent course staff and are now available on MIT OpenCourseWare.