Modern laboratory equipment produces huge amounts of experimental data — spectral vectors with 100’s or 1000’s wavelengths, microarrays, gene expression data, multi-channel images and many others. Even conventional measurements may end up with tens of variables. Such data represent a wealth of potential information but usually only a part of it relates to a problem of interest. This course teaches how to extract problem-dependent information from multivariate data; there is no upper limit to the number of variables involved, the methods are dimensionally invariant.
This course has five main topics: descriptive statistics, exploratory data analysis, regression and validation, preprocessing and variable selection and pattern recognition. In each part there are 2-4 lectures supplemented with a suite of real life examples and exercises.
Each topic also has corresponding practice data sets, with which students will try the discussed methods by solving various data analysis problems. Alternatively
Associate Professor Sergey Kucheryavskiy, email: email@example.com
Associate Professor Sergey Kucheryavskiy
April 8-12, 2013
Section of Chemical Engineering, Aalborg University, campus Esbjerg, Niels Bohrs Vej, 8
Number of seats:
March 25, 2013
Important information concerning PhD courses
We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.
- Teacher: Sergey Kucheryavskiy