each summer i organize and teach the microsoft research data science summer school, an eight-week hands-on introduction to data science for college students in the New York City area to promote diversity in computer science.
i'm currently teaching modeling social data (apam e4990) in columbia's applied mathematics department (spring of 2019). i've taught this course in 2017 and 2015 as well.
i co-taught computational social science (apam e4990) in columbia's applied mathematics department in the spring of 2013.
i taught data-driven modeling (apam e4990) in columbia's applied mathematics department in the spring of 2012, a course on data mining and applied machine learning. see the current course site and related code for more information.
i originally designed and taught this class in the fall of 2009.
i gave a (rather impromptu) tutorial on machine learning at the 2007 boulder summer school for condensed matter and materials physics. the slides and associated demos are availble for download at the school's google group.
i'm lucky enough to have taught a biological physics course for
columbia's saturday morning
science honors program
for high school students. more material to come in the future,
but here are a few things:
some questions given out on the first day of class
matlab code for a 1-d diffusion demo
matlab code for a 1-d random walk demo
homework solution, derivation of the gaussian approximation to the binomial
after surviving columbia's qualifying exam i decided to
organize a prep course for first year grad students
in columbia's physics phd program. the the result was a
(hopefully useful) set of problems that were
discussed by each week.
here's the old version of the quals prep site that has some study guides and references listed. also, here are some (mostly stolen) example problems for the electricity and magnetism, modern physics, mechanics, and general physics sections of the exam.
note: columbia's quals are undergrad level.