Applied Machine Learning and Big Data

• Understand and apply multidimensional data representations.
• Understand and apply methods for cleaning of datasets.
• Understand and apply machine leaning for visualization of datasets.
• Understand and apply machine learning for cluster analysis (unsupervised classification) of dataset.
• Understand and apply machine learning for classification.
• Understand and apply cloud services for big data analysis.
• Understand and apply fundamental Unix/Linux tools for administration of server platform for big data.
• Understand and apply a scientific database, e.g. DTU Findit for search of references targeted a given project area.

Data representation in multidimensional heterogeneous datasets.
Cleaning of dataset, including identification and removal of outliers.
Application for machine learning based visualization; cluster analysis
e.g. k-nearest-neighbor, hierarchical clusteranalysis, spectral clustering, naiive Bayes;
and classification e.g. logistic regression, support vector machines, decision trees, random forests, deep neural networks, recurrent neural networks.
Applying cloud services for big data analysis, including tools for administration of a server platform.
Applying a scientific database for knowledge.

You must have programming knowledge and desire to learn more programming. Bring your own PC (Mac, Windows or Linux). Depending on your data size you must expect to allocate funds for the storage and processing of the data.

You must have basic knowledge about Linear Algebra (such as vector/matrix multiplication/inverse, eigenvalues/​eigenvectors) and Probability Theory (such as probability density function, probability distributions, central limit theorem, bayes theorem).

Lectures and project work

Section of AI, Mathematics and Software
Sundhedsteknologi: valgfag
IT-elektronik:valgfag
Softwareteknologi:valgfag
ITØ:valgfag

Minimum: 6, Maximum: 15.

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.