Computational Data Analysis

• Evaluate clustering methods and select suitable parameters and models for given data
• Evaluate linear discriminant analysis and ridge regression
• Evaluate cross validation, bootstrapping and concepts such as overfitting.
• Evaluate sparse regression and classification models.
• Evaluate logistic regression and support vector classifiers for 2 class problems
• Evaluate and interpret Classification and Regression Trees (CART).
• Evaluate bagging, boosting and random forests for classification and regression.
• Evaluate and interpret sparse latent methods such as sparse principal component and sparse partial least squares.
• Evaluate a range of unsupervised decomposition methods
• Compare the mentioned methods theoretically

Methods: Clustering, ridge regression, elastic net, sparse principal components, sparse discriminant analysis and Gaussian mixture analysis, logistic regression, support vector machine, classification and regression trees, bagging, boosting, random forests, nonnegative matrix factorization, independent component analysis, sparse coding, archetypical analysis, multivay methods, functional data analysis.
Case studies: Varies each year.

02409/02450/27411, It suffices with one of the above mentioned courses. Alternatively a similar course in advanced statistics or data analysis. Knowledge of Matlab, R or Python is an advantage.

Thirteen weeks of lectures and data bar exercises. The activities alternate between hands-on exercises and lectures. Additionally, two cases are carried out.

Minimum: 8, Maximum: 300.

Please be aware that this course has a minimum requirement for the number of participants needed, in order for it to be held. If these requirements are not met, then the course will not be held. Furthermore, there is a limited number of seats available. If there are too many applicants, a pool will be created for the remainder of the qualified applicants, and they will be selected at random. You will be informed 8 days before the start of the course, whether you have been allocated a spot.