GraduateClassFlier < Teaching

Statistical learning theory, including powerful Bayesian methods like graphical modeling, has transformed artificial intelligence and expert systems
Some of the biggest growth applications of this area within experimental science are in bioinformatics and systems biology
These areas use technologies complementary to graphical models, such as
- stochastic grammars for biological sequence analysis (also used in speech, text and source code processing)
- continuous-time Markov models for molecular, evolutionary and cellular dynamics (or keystrokes, web clicks, etc)
- Gaussian processes for spatially and temporally fluctuating data, e.g. gene expression (as used in geostatistics)
- possible advanced topics: neural networks, Markov random fields, imaging...
Learn probabilistic methods applicable to a wide range of problems, using fascinating examples from evolutionary molecular biology
A course for engineers
Spring 2005
Read more at http://biowiki.org/view/Teaching/GraduateClass