An introduction to probability as a means of representing and reasoning with uncertain knowledge, with an emphasis on graphical probability models. Topics covered will include: the formalism of probability and its interpretations, qualitative specification of probability distributions by means of independence relationships expressed using graphical models, quantitative specification of probability distributions parameterized using graphical models, algorithms for probabilistic reasoning with graphical models, elicitation of probability models from experts, learning probability models from empirical data, inferring causal relationships, alternative, non-probabilistic formalisms for expressing uncertain or imprecise knowledge.
Here is the course outline: Postscript, PDF.
Here is the probability puzzle from the first lecture.
Here is the assignment: Postscript, PDF.
Papers to read:
Kschischang, et al, ``Factor graphs and the sum-product algorithm'': Postscript, PDF.You can now get the "final" version of my software for Low Density Parity Check codes, updated 2000-03-19.