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Computational data analysis


Technical University of Denmark


General course objectives:
To provide the student knowledge of advanced computer intensive data analysis methods with applications to e.g. life sciences. These include problems with many variables and relatively few observations etc.

Learning objectives:
A student who has met the objectives of the course will be able to:
  • Relate parts of the course to the student's own project
  • Evaluate cross validation and concepts such as overfitting
  • Evaluate and apply sparse regression and classification models
  • Evaluate and apply logistic regression and support vector machines
  • Evaluate and apply Classificaiton and regression trees (CART)
  • Evaluate and apply random forests, boosting and ensemble methods
  • Evaluate and ainterpret sparse latent methods such as sparse principal component analysis
  • Evalute and interpret a range of unsupervised decomposition methods
  • Evaluate clustering methods
  • Compare and choose between the above methods

Contents:
Methods: Cross-validation, elastic net, sparse principal components, sparse discriminant analysis and Gaussian mixture analysis, logistic regression, support vector machine, classification and regression trees, random forests, clustering, nonnegative matrix factorization, independent component analysis, sparse coding, archetypical analysis.

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Lecturer
Line Katrine Harder
Place/Venue
Anker Engelunds Vej 1
City
Kgs. Lyngby
Country
Denmark
ECTS
5 points
Link
http://www.kurser.dtu.dk/02910.aspx?menulangu...
Language
English
Block-scheduling
No
Cost
Not available