In the first part of the workshop we want to introduce into methods of data analysis and parameter estimation. Given a set of experimental results, one often wants to condense and summarize the data by fitting it to a “model” that depends on adjustable parameters. The „model" may simply be a given function, such as a gaussian, or be a complex theoretical model describing e.g. the dynamics of a signaling pathway.  

Particularly, we explain the concepts of maximum likelihood estimators, linear and nonlinear regression, and various search algorithms. We will also explain some basic concepts of machine learning methods, which provide flexible approaches to tasks like identifying objects in images.