Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
|
lopro [2011/09/25 14:19] val.tannen |
lopro [2011/09/25 14:24] val.tannen |
||
|---|---|---|---|
| Line 2: | Line 2: | ||
| **Doctoral course by Val Tannen** | **Doctoral course by Val Tannen** | ||
| + | |||
| + | lecture notes 1 [[http://www.cis.upenn.edu/~val/LogicProbEPFL2011files/ln1.pdf|(pdf)]] | ||
| //Syllabus// | //Syllabus// | ||
| Line 9: | Line 11: | ||
| *A bit of the story of how Mathematical Logic (and Electronic Technology) gave birth to Computer Science. | *A bit of the story of how Mathematical Logic (and Electronic Technology) gave birth to Computer Science. | ||
| - | -Part I: Probability of Logic | + | *Part I: Probability of Logic |
| - | -Random graphs and random structures | + | *Random graphs and random structures |
| - | -0-1 laws | + | *0-1 laws |
| - | -Queries on probabilistic databases | + | *Queries on probabilistic databases |
| - | -Part II: Logic of Probability | + | *Part II: Logic of Probability |
| - | -Pearl-Paz logics for reasoning about probabilistic (in)dependence | + | *Pearl-Paz logics for reasoning about probabilistic (in)dependence |
| - | -Reasoning about independence in Bayesian and Markov networks; the Hammersley-Clifford theorem | + | *Reasoning about independence in Bayesian and Markov networks; the Hammersley-Clifford theorem |
| - | -Reasoning about independence in relational graphical models (PRMs and RMNs) | + | *Reasoning about independence in relational graphical models (PRMs and RMNs) |