(1) 1-12-26: Course overview; introduction to nonequilibrium thermodynamics:
Video,
Slides.
(2) 1-14-26: Course overview continued, coarse-graining:
Video.
Coarse-graining physical systems (micro- vs. macro-states, seven ways of looking at a protein, turtles all the way down):
Slides.
(3) 1-16-26: Trajectories and ensembles, the basics of probability theory, Bayes' rule:
Video,
Notes.
(4) 1-21-26: Understanding Bayes' rule: posterior, prior, and likelihood; fitting models to data:
Video,
Notes.
(5) 1-23-26: Application of Bayesian model fitting: dense neural networks:
Video,
Notes.
(6) 1-28-26: The Markovian assumption and its consequences: calculating the probabilities of trajectories and states, master equation:
Video,
Notes.