Undergraduate
Next session in Fall 2013 Graduate
This course is about theoretical ecology. I present modeling methods for ecosystems studies, including mathematical modeling and individual based modeling, illustrated with many examples of applications. I particularly focus on our approach, EcoSim, which is an individual based approach to model a large predator-prey evolving ecosystem. I show examples of researches accomplished with this model like study of speciation mechanism, diffusion of diseases, emergence of complex behaviors, measurement of chaotic behavior of the simulation... There is no biological prerequisite for this course as I present all the notions needed. Students from Biology with programming skills are welcome. Next session in Fall 2013
Focusing on several central bioinformatics problems, this course
presents important machine learning techniques (including feature selection,
classification, clustering and probabilistic model building) and optimization
methods (such as expectation maximization, simulated annealing, tabu search or
evolutionary algorithm) that are commonly used to solve these problems. The
efficiencies and limitations of each of them will be discussed and the
correlation between the different approaches will be highlight.
This introductory course discusses the
development and use of computer science techniques to help solve
problems in molecular biology. The purpose is to present a
representative sample of computational problems in molecular biology and
some of the efficient algorithms that have been proposed to solve them.
Topics include: sequence comparisons, database search, DNA fragments
assembly, DNA mapping, phylogenetic trees, genome rearrangements,
molecular structure prediction, DNA computing. Student will be required
to investigate selected problems/methods in computational biology. |