- 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
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