NDAB16012U Modelling and Analysis of Data (MAD)

After the course the student should have the following knowledge, skills, and competences.

Knowledge:

The student will have knowledge about multivariate calculus as well as data-analysis techniques including data-representation, filtering, modelling and estimation, and visualisation.

Skills:

The student will be able to

* Compute partial derivatives and gradients and use these for both filtering, optimization and derivation of data analysis algorithms
* Choose an appropriate data representation, and transform between space/time and frequency domains, filter in both space/time- and frequency domains.
* Apply the least squares method for linear modeling and estimation.
* Analyze sampled data by appropriate mathematical modelling methods
* Describe certain useful multivariate methods and their use, especially principal component analysis (PCA) and its use in dimensionality reduction.
* Visualize low- and high-dimensional data by simple plots and images.
* Implement simple data analysis and modeling methods.
* Perform the analysis of experimental data using the methods learnt during the course and evaluate the results.

Competences:

The student will be capable of performing basic data-analysis tasks which include modelling, visualisation, and interpretation of the results and the describing limitations of the used methods. The student will be able to use calculus tools such as partial derivatives, gradients and integrals, both directly in image processing or optimization, but also analytically for deriving analytical algorithms for data analysis.